Accepted Manuscript

The use of Vis/NIRS and chemometric analysis to predict fruit defects and post-harvest behaviour of ‘Nules Clementine’ mandarin fruit

Lembe Samukelo Magwaza, Sandra Landahl, Paul J.R. Cronje, Hélène H.Nieuwoudt, Abdul Mounem Mouazen, Bart M. Nicolaï, Leon A. Terry,Umezuruike Linus Opara

PII: S0308-8146(14)00655-4DOI: http://dx.doi.org/10.1016/j.foodchem.2014.04.085Reference: FOCH 15747

To appear in: Food Chemistry

Received Date: 24 August 2013Revised Date: 22 April 2014Accepted Date: 23 April 2014

Please cite this article as: Magwaza, L.S., Landahl, S., Cronje, P.J.R., Nieuwoudt, H.H., Mouazen, A.M., Nicolaï,B.M., Terry, L.A., Opara, U.L., The use of Vis/NIRS and chemometric analysis to predict fruit defects andpostharvest behaviour of ‘Nules Clementine’ mandarin fruit, Food Chemistry (2014), doi: http://dx.doi.org/10.1016/j.foodchem.2014.04.085

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1

The use of Vis/NIRS and chemometric analysis to predict fruit defects and postharvest 1

behaviour of ‘Nules Clementine’ mandarin fruit 2

3

Lembe Samukelo Magwazaa,b*, Sandra Landahlc, Paul J.R. Cronjed, Hélène H. Nieuwoudte, 4

Abdul Mounem Mouazenf, Bart M. Nicolaï g, Leon A. Terryc, Umezuruike Linus Oparab 5

6

a Department of Crop Science, School of Agricultural, Earth and Environmental Sciences, 7

University of KwaZulu-Natal, Private Bag X01, Scottsville, 3209, Pietermaritzburg, South 8

Africa 9

10

b Postharvest Technology Research Laboratory, South African Research Chair in Postharvest 11

Technology, Stellenbosch University, Stellenbosch 7602, South Africa. 12

13

c Plant Science Laboratory, Cranfield University, Bedfordshire MK43 0AL, United Kingdom 14

15

d Citrus Research International, Department of Horticultural Science, Stellenbosch 16

University, Stellenbosch 7602, South Africa. 17

18

e Institute for Wine Biotechnology, Department of Viticulture and Oenology, Stellenbosch 19

University, Stellenbosch, 7602, South Africa. 20

21

f Department of Environmental Science and Technology, Cranfield University, Bedfordshire, 22

MK43 0AL, United Kingdom. 23

24

g MeBioS division, Department of Biosystems, Katholieke Universiteit Leuven, Kasteelpark 25

Arenberg 30, B-3001 Leuven, Belgium. 26

27

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*Corresponding author: Lembe Samukelo Magwaza, 29

Phone: +27 21 808 4064; Fax: +27 21 808 3743. 30

Email: magwazal@ukzn.ac.za and lembe.magwaza@gmail.com 31

32

33

2

Abstract 34

35

The use of chemometrics to analyse Vis/NIRS signal collected from intact ‘Nules 36

Clementine’ mandarin fruit at harvest, to predict the rind physico-chemical profile after eight 37

weeks postharvest was explored. Vis/NIRS signals of 150 fruit were obtained immediately 38

after harvest. Reference data on the rind were obtained after eight-week storage, including 39

colour index (CI), rind dry matter (DM), and concentration of sugars. Partial least squares 40

(PLS) regression was applied to develop models. Principal component analysis (PCA) 41

followed by PLS-discriminant analysis (PLS-DA) were used to classify fruit according to 42

canopy position. Optimal PLS model performances for DM, sucrose, glucose and fructose 43

were obtained using multiple scatter correction pre-processing, showing respective residual 44

predictive deviation (RPD) of 3.39, 1.75, 2.19 and 3.08. Clusters of sample distribution in the 45

PCA and PLS-DA models based on canopy position were obtained. The results demonstrated 46

the potential applications of Vis/NIRS to predict postharvest behaviour of mandarin fruit. 47

48

Chemical compounds studied in this article: 49

Sucrose (PubChem CID: 5988); Glucose (PubChem CID: 5793); Fructose (PubChem 50

CID: 5984) 51

52

Keywords: 53

Rind breakdown; Visible-NIR spectroscopy; Citrus; Non-destructive; Spectral pre-54

processing; Rind physiological disorder; Postharvest technology. 55

56

57

3

1. INTRODUCTION 58

59

External appearance is the primary parameter used to evaluate quality of citrus fruit and the 60

presence of skin defects is one of the most influential factors in determining the price of fresh 61

fruit. Citrus fruit quality classification is currently based on the evaluation of fruit surface 62

colour, size, shape and freedom from defects. These quality parameters are usually evaluated 63

by humans or by machine vision systems (Xudong, Hailiang, & Yande, 2009). Under most 64

grading systems, fruit with slight external defects are graded and marketed with sound fruit, 65

thereby reducing the quality of the batch. Alternatively, fruit with slight defects are graded 66

out and removed together with seriously damaged fruit, thus causing economic losses 67

(Blasco, Aleixos, & Moltó, 2007). The challenge is significant regarding citrus rind disorders 68

that do not manifest at harvest or during grading, but develop about 1-5 weeks after harvest; 69

such as rind breakdown disorder (RBD) of ‘Nules Clementine’ mandarins (Citrus reticulata 70

Blanco) (Magwaza et al. 2012a; Magwaza et al. 2013a, Magwaza, Opara, Cronje, Nicolaï, 71

Landahl, & Terry, 2013b). The challenge is therefore to develop a non-destructive technology 72

that will determine rind quality in the packing line and assist in pre-symptomatic sorting and 73

segregation of fruit into different quality tiers. 74

75

Non-destructive optical methods based on visible to near-infrared (Vis/NIR) spectroscopy 76

(Vis/NIRS) have been developed and evaluated for non-destructive internal quality 77

assessment of fruit and vegetables, including citrus (Antonucci, Pallottino, Paglia, Palma, 78

D’Aquino, & Menesatti, 2011; Magwaza et al., 2013c). Very limited research work has been 79

conducted to develop a technology that can assess, predict and monitor fruit rind 80

physiological disorders. However, the success of Vis/NIRS to detect oleocellosis and decay 81

in citrus fruit (Zheng et al., 2010; Gómez-Sanchis, Lorente, Soria-Olivas, Aleixos, Cubero, & 82

4

Blasco, 2013), suggests the potential of this technology in determining the sensitivity or 83

propensity of specific citrus fruit consignments to develop non-chilling associated 84

physiological rind disorders. The trend is constantly shifting towards developing more 85

reliable and cost effective technologies to non-destructively screen fruit physiological 86

disorders. 87

88

Vis/NIRS depends on chemometrics which involves multivariate analysis for interpreting 89

large data sets (Wang & Paliwal, 2007; Escuredo, Seijo, Salvador, & González-Martín, 90

2013). Currently, partial least squares (PLS) regression (PLSR) is probably the most widely 91

applied regression method in chemometrics, and the approach has been used to evaluate the 92

potential of Vis/NIRS in measuring the quality characteristics of ‘Satsuma’ mandarin fruit 93

(Gómez, He, & Pereira, 2006). Besides resulting in better prediction models, PLSR results in 94

models which always have the lowest number of latent variables (LVs) since PLSR models 95

exclude LVs that are not important to describe the variance of the quality parameter (Gòmez 96

et al., 2006). 97

98

The spectra of solid and scattering samples such as intact fruit are influenced by physical 99

properties such as shape and size. This creates noise problems when analysing quality 100

parameters for which such physical characteristics are not important (Magwaza, Opara, 101

Nieuwoudt, Cronje, Saeys, & Nicolaï, 2012b). In order to remove these baseline shifts, 102

facilitate handling and to develop more simple and robust models, the complex spectral data 103

are often pre-treated by different statistical procedures (Nicolaï et al., 2007). Thus, the 104

selection of suitable pre-processing or pre-treatment methods is an important step in the 105

process of spectral analysis. 106

107

5

The use of Vis/NIRS and chemometrics to evaluate fruit physiological defects and predict 108

postharvest quality attributes of fruit are topical in postharvest research. In this study, the 109

application of chemometric analysis on spectra of intact ‘Nules Clementine’ mandarin fruit at 110

harvest to predict future rind biochemical profile and to predict susceptibility of fruit rind to 111

develop RBD was investigated. 112

113

2. Materials and methods 114

115

2.1. Fruit samples 116

117

The study was conducted in 2012 using ‘Nules Clementine’ mandarin (Citrus reticulata 118

Blanco) fruit harvested from a commercial orchard located in Paarl area of the Western Cape 119

Province, South Africa (33°43’27.44’’S; 18°57’21.28’’E). A total of 100 fruit (50 from the 120

inside position and 50 from the outside position of the canopy) from 10 trees were selected 121

for non-destructive and destructive measurements. Of the 100 fruit harvested, 60 were used 122

for calibration and the remaining 40 were used for validation set during model development. 123

An independent population of 50 fruit (for validation) was harvested from a commercial 124

orchard in Citrusdal, Western Cape Province, South Africa (32° 35' 18.26'' S, 19° 1' 14.69'' E) 125

using the selection procedure described above. Fruit were harvested at optimum maturity 126

according to industry practice, then received all commercial postharvest practices, including 127

drenching (Thiabenzole, 500 mg/L; Imazalil, 500 mg/L and 2,4-dichlorophenoxyacetic acid, 128

125 mg/L) and waxing (polyethylene citrus wax, Citrushine®, Johannesburg, South Africa). 129

After phytosanitary inspection and export certification, fruit from different canopy positions 130

were packed in separate carton boxes, sent at ambient temperature via a courier service to 131

Cranfield University (CU) in the United Kingdom, where experiments were conducted. Fruit 132

6

arrived at CU after 48 hours and were stored for 24 hours at 20°C and 80% relative humidity 133

to equilibrate, prior to taking NIR measurements. 134

135

2.2. Spectral acquisition 136

137

Upon arrival at CU, spectra of intact fruit samples were acquired using a method described by 138

Magwaza et al. (2012a). Briefly, spectral data was collected using a mobile fibre-optic 139

spectrophotometer (350-2500 nm) (LabSpec2500® Near Infrared Analyzer, Analytical 140

Spectral Devices Inc., USA) in diffuse reflectance mode, equipped with one Si array (350-141

1000 nm) and two Peltier cooled InGaAs detectors (1000-1800 nm and 1800-2500 nm). 142

Reflectance spectra were acquired from 8 positions on the fruit; 4 from equatorial spots, 2 143

from the stem-end and 2 from the stylar-end of the fruit. The first set of spectra was acquired 144

before storage and the second set was acquired from the same fruit (and the same spots within 145

the fruit) after eight weeks of cold storage. 146

147

2.3. Storage and destructive (reference) measurements 148

149

After the first round of spectral acquisition fruit were stored for 8 weeks in a cold room with 150

delivery air temperature of 8±0.5°C which is known to cause the highest degree of RBD 151

incidence (Magwaza et al., 2013b). Destructive data on physico-chemical properties 152

including colour, RBD, DM and non-structural carbohydrates (sucrose, glucose and fructose) 153

of fruit were obtained after 8 weeks of storage. Rind colour components were measured in 154

L*a*b* colour space using a Minolta CR-400 colorimeter (Chroma Meter CR-400, Konica 155

Minolta Sensing Inc., Japan) after calibration using a standard white tile (CR-A43; Y = 93.1, 156

7

x = 0.3138; y = 0.3203). From the L*, a* and b* colour parameters, the colour index (CI) 157

was calculated as follows: (Pathare, Opara, & Al-Said, 2013). 158

159

b x L

a x 1000=CI (1) 160

161

During cold storage, fruit were scored weekly for the incidence of RBD, over eight weeks. 162

RBD incidence was scored on a subjective scale from 0 = no breakdown to 3 = severe 163

breakdown (Fig. S1). RBD was then expressed as RBD index as described by Alférez & 164

Burns (2004) in the following formula: 165

166

fruit ofnumber Total

class}each in fruit No.of ×)30({∑ −=

RBDRBDindex (2) 167

168

After storage, rind was peeled by hand from the rest of fruit, snap frozen in liquid nitrogen 169

and stored at -40°C until further analysis. Frozen samples were then freeze-dried in a 170

Labogene ScanVac CoolSafe Freeze Dryer System (CS55-4, Denmark) for 7 days at 0.015 171

kPA and -55°C. Lyophilized samples were weighed and water content was calculated from 172

freeze dried samples and expressed as a percentage of fresh weight, after which dried samples 173

were ground using a pestle and mortar into fine powder. Non-structural carbohydrates 174

(sucrose, glucose and fructose) were extracted and quantified using a method described 175

elsewhere (Magwaza, Opara, Cronje, Landahl, & Terry, 2013d).

176

177

2.4. Data analysis 178

179

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Statistical analysis of destructive measurements was carried out using SPSS 20.0 for 180

Windows (SPSS Inc. Chicago, USA). Data were subjected to analysis of variance (ANOVA). 181

Least significant difference values (LSD; p=0.05) were calculated for the comparison of 182

means. 183

184

2.5. Chemometrics 185

186

The reflectance spectra in Indico format (Indico Pro 5.6 software, Analytical Spectral 187

Devices Inc., USA) were transformed to absorbance (log (1/R)). Individual spectra from 8 188

positions within the fruit and the average spectra of 8 spectra from each fruit were tested to 189

develop PLS, PCA and PLD-DA models. Average spectra showed better models than 190

individual spectra (data not shown); and thus results reported herein are based on average 191

spectra. Calculations of the average of 8 spectra obtained from each fruit, pre-processing and 192

calibration methods were executed using The Unscrambler® chemometric software (Version 193

9.2, Camo Process, SA., Norway). 194

195

Different pre-processing methods including MSC, SNV, Savitzky Golay first derivative and 196

second derivative (2nd

order polynomial), were applied to normalise and smooth spectral data 197

prior to regression to correct for light scatter, reduce the changes of light path length. 198

199

Average Vis/NIRS signals were subjected to PCA to determinate effective wavelength, detect 200

outliers and to discriminate fruit from different canopy positions. PLS 1 regression/prediction 201

models were developed using spectral data for each quality attribute. A PLS variant known as 202

partial least squares discriminant analysis (PLS-DA or PLS 2) was also used in order to 203

classify fruit from different canopy positions according to the spectra. A method by Chen, 204

9

Wu, He, & Liu (2011) with slight modifications was used in the application of PLS-DA. Fruit 205

from each of the canopy positions in the calibration set was assigned a dummy variable as a 206

reference value (outside = 1 and inside = 2). In addition, due to discrete nature of RBD 207

scores, samples were assigned a binary dummy variable as a reference value, which was an 208

arbitrary number whether the sample belongs to a particular position or not. RBD affected 209

fruit were set as reference data one (1), while unaffected fruit were assigned to 0. 210

211

During model development, the dataset was subjected to test set validation where fruit were 212

randomly separated into two subsets, 60% for calibration and 40% for validation. Although 213

the sample selection method for calibration and validation was random, validation data sets 214

were scrutinised to ensure that the validation data sets were confined within a range of values 215

of the calibration set. The regression statistics of developed models was described by the 216

value of the root mean square error of calibration (RMSEC), root mean square error of 217

validation or prediction (RMSEP), the correlation coefficient (R), which represents the 218

proportion of explained variance of the response variable in the calibration (Rc) or validation 219

dataset (Rv). The number of LVs, and the residual predictive deviation (RPD), described by 220

Williams & Sobering (1996) as the ratio of the standard deviation of the destructive data for 221

the validation set to the RMSEP. The ideal model should have high R and RPD values as well 222

as low RMSEC and RMSEP values. The optimal number of LVs was determined as the 223

minimum number of LVs corresponding to the first lowest value of the RMSEC or RMSEP 224

from the plot of the RMSEC or RMSEP for increasing number of LVs (Davey, Saeys, Hof, 225

Ramon, Swennen, & Keulemans (2009). 226

227

The spectral variables which contributed the most to the model were determined from the 228

regression coefficients curve. Wavelength bands with high regression coefficient values are 229

10

important to the mode. Outliers were evaluated using the score plots, X-residuals and 230

leverage plots on the PLS and PCA models (Kuang & Mouazen, 2011). Samples that were 231

located far from the zero line of the residual variance plot were identified as outliers and only 232

2 spectral outliers were identified and excluded. 233

234

The stability of the calibration model was tested by interchanging validation and calibration 235

data sets and checking that the differences in the regression statistics obtained were small 236

(Alvarez-Guerra, Ballabio, Amigo, Bro, & Viguri, 2010). Prediction model robustness was 237

tested by external validation with spectra of fruit harvested from a farm in Citrusdal; an 238

orchard located about 100 km from the orchard of fruit used during model development. 239

240

3. Results and discussion 241

242

3.1. Description spectra 243

244

The reflectance spectra presented in Fig. S2 portray the typical spectra obtained from intact 245

‘Nules Clementine’ mandarins harvested from different canopy positions. Each line 246

represents the average spectra acquired from 50 fruit in each canopy position before and after 247

storage, respectively. Spectral features such as reflectance peaks were similar to those 248

obtained by Gómez et al. (2006). The beginning (350–450 nm) of each spectrum was 249

characterised by noise and was removed before calibration. Strong absorption bands around 250

670, 740, 980, 1200, 1450, 1780 and 1930 nm were observed. Absorption at 670 nm is due to 251

red absorbing pigments, particularly chlorophyll (Clément, Dorais, & Vernon, 2008); 740 nm 252

corresponds to third overtone of O-H stretching; 980 nm is associated with second overtone 253

of H-O-H stretching modes of water; 1200 is the combination of second overtones of C-H 254

11

and C-H2 stretching; 1450 belongs to the first overtone of O-H stretching; 1780 nm is a 255

combination of first overtone of C-H and CH2 stretching; and 1930 nm is the combination of 256

O-H, C-H and C-H2 deformations associated with sugar solution (Kawano et al., 1993; Golic, 257

Walsh, & Lawson, 2003; Tewari, Dixit, Chi, & Malik, 2008). It should be noted that before 258

storage, the average spectra of fruit from outside canopy had a distinctly stronger absorbance 259

in the waveband between 590 and 900 nm compared to fruit from inside canopy position. 260

After 8 weeks of storage, the difference between spectral data acquired from fruit outside and 261

inside canopy positions was less pronounced due to loss of chlorophyll (green pigment) 262

during storage. Results observed in this study are similar to those obtained by Zheng et al. 263

(2010) for prediction of oleocellosis disorder, where large variations in absorbance spectra 264

were observed among fruit with different sensitivities in the same waveband between 590 and 265

900 nm. In this current study, fruit harvested from the inside position of the canopy were 266

more susceptible to RBD than outside fruit, a trend that is similar to previous work (Cronje, 267

Barry, & Huysamer, 2011, 2013; Magwaza et al., 2012a; 2013d). Since the intensities of 268

reflectance vary with concentrations of biochemical constituents of the sample, the band may 269

possibly be related to fruit sensitivity to rind physiological disorders such as oleocellosis and 270

RBD. 271

272

3.2. Description of destructive data for Vis/NIRS calibrations 273

274

The distributional statistics of destructive data used in calibration and independent validation 275

are summarized in Table 1. The reference measurements for calibration and validation data 276

sets were normally distributed around the means, covered a wide range and had enough 277

variation, presented by the coefficient of variation (CV%). High variation of the reference 278

data is helpful in developing reliable prediction models for Vis/NIRS (Clément et al., 2008). 279

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The mean values of the CI for calibration and validation populations were 4.38 and 4.42, 280

respectively, with the corresponding CV% values of 37.81 and 35.75%. The mean 281

concentration of sucrose values used for calibration and validation were 88.19 and 92.68 282

mg/g DW with standard deviation of 41.63 and 42.73 mg/g DW, respectively. The 283

concentration of total non-structural carbohydrates in the rind tissue ranged between 122.03 284

and 501.12 mg/g DW in the calibration population and the range of validation set was from 285

137.66 to 512.01 mg/g DW with corresponding CV% of 34.25 and 32.30, respectively. 286

287

3.3. Spectral pre-processing and wavelength selection 288

289

Similar to our previous study (Magwaza et al., 2012a, Magwaza, Opara, Cronje, Nieuwoudt, 290

Landahl, & Terry, 2013e), the models constructed using individual spectra from 8 positions 291

within the fruit were unacceptably poor (data not presented). In all cases, average spectra 292

showed better model performance than individual spectra from each position. Hence, in the 293

current study, the average spectra were used to develop PLS, PCA and PLS-DA models. Poor 294

performance of individual spectra could be the result of spatial distribution and level of 295

attributes within the fruit. In citrus and other fruit types, chemical composition has been 296

reported to vary from stem to blossom end, from sun to shade sides, and from different 297

canopy positions of the fruit (Peiris, Dull, Leffler, & Kays, 1999). This suggests that Vis/NIR 298

spectra acquisition needs to be repeated at several positions around the fruit in order to 299

minimise the effect of variation within fruit. However, this might not be practically 300

compatible with a typical speed of commercial sorting lines, which may be as high as 10 fruit 301

per second (Nicolaï et al., 2007). Parallel installed spectrometers might overcome this 302

problem. 303

304

13

Spectral data of solid and scattering samples such as intact mandarin fruit are complex as 305

they are influenced by physical properties such as shape, size, etc (Magwaza et al., 2012b). 306

As such, spectral pre-processing facilitates handling and development of simpler and more 307

robust models. After testing several pre-processing methods, prediction models that gave the 308

higher R, lower RMSEC and RMSEP, a small difference between RMSEC and RMSEP and 309

high RPD was selected to predict the quality parameter of interest (Lammertyn, Peirs, De 310

Baerdemaeker, & Nicolaï, 2000). 311

312

Results in Table 2 show that CI was best predicted using raw spectra without spectral pre-313

processing in the visible region (450-750 nm). Since colour parameters are likely to be 314

directly detected in the visible range, it is logical that model performance for CI did not need 315

spectral pre-processing. The CI results demonstrated a classic example of the risk associated 316

with removing useful information from the data by spectral pre-processing. The best stable 317

model for predicting rind DM (RPD = 3.39) was achieved using models developed by MSC 318

pre-processing method. In the case of rind DM, the model based on Savitzky-Golay first 319

derivative (fifth order polynomial) pre-processing also had acceptable prediction potential 320

(RPD = 3.47), but MSC had the smallest difference between Rc and Rv, and was thus more 321

reliable. Although rind DM was best predicted based on spectra treated by MSC, the results 322

obtained using Savitzky Golay first derivative spectral pre-processing were marginally lower, 323

suggesting that both methods could be used for rind DM prediction of ‘Nule Clementine’ 324

mandarin rinds. 325

326

MSC also gave best results for PLS prediction of rind carbohydrates concentration such as 327

sucrose, glucose fructose, and total carbohydrates with RPD values of 1.75, 2.19, 3.08, and 328

3.06, respectively. Savitzky-Golay second derivative with the second order polynomial 329

14

provided the better results for the PCA classification of fruit from different canopy positions 330

(Fig. 1) and PLS model for predicting RBD (Fig. S3). Previous research showed that 331

Savitzky-Golay first and second derivative corrected light scattering properties while MSC 332

corrected for additive, multiplicative effects of the spectra, and pathlength variations (Gómez 333

et al., 2006). It is therefore important to note that the rind DM, fructose, glucose, sucrose and 334

total sugars models developed in the succeeding sections are based on Vis/NIRS after MSC 335

pre-processing while CI models were developed without pre-processing. 336

337

The optimal number of LVs was determined as the minimum number corresponding to the 338

first lowest value of the residual y-variance, from the plot of residual y-variance against 339

number of LVs (Fig. 2A) (Davey et al., 2009). In the rind DM PLS model, the optimum 340

number of LVs was observed as eight. The residual variance did not change after LV 8; 341

hence adding another LV explained very small variance and would have resulted in a 342

complex model. With regard to fructose, the number of LVs (14) used to construct the 343

models were too high and violated the statistical rule of thumb, which states that the ratio of 344

the number of samples for calibration (in this case 100) to the number of LVs should be equal 345

to or larger than 10 (Lammertyn et al., 2000). Despite this large number of LVs used to 346

construct the model, the model seemed to be accurate and stable during the validating 347

exercise. The regression co-efficient curve in Fig. 2B also confirmed that only significant LVs 348

were modelled and noise was not included in the calibration models. 349

350

The contributions of spectral variables to the model were determined from its regression 351

coefficients curve. Wavelength bands with high absolute regression coefficient values are 352

important for the model while regression coefficients with a value close to zero do not 353

contribute to the model (Magwaza et al., 2013c). A typical regression coefficient curve 354

15

obtained during prediction model development for dry matter content in ‘Nules Clementine’ 355

mandarin rinds is portrayed in Fig. 2B. High absolute regression coefficients were observed 356

in the NIR region of the curve at 950, 1200, 1320 and 1700 nm. As mentioned above, these 357

high absolute regression coefficients correspond with the second overtone of H-O-H 358

stretching modes of water, the second overtone of C-H and C-H2 stretching related to sugars 359

molecules as well as second and third overtones of OH and CH stretching vibrations of water 360

hydrogen bonds with sugar molecules related to vibration of water hydrogen bonds with 361

sugars molecules (Kawano et al., 1993). From the regression coefficients results, the 362

informative wavelength bands for all rind carbohydrates and DM were between 900-1700 363

nm, while CI and RBD were best predicted at 450-750 and 450-1000 nm, respectively. 364

365

3.4. Vis/NIRS-based PCA and PLS models 366

367

PCA was performed on Vis/NIRS spectra to compare the characteristics of fruit from 368

different positions on the tree. The PCA was applied to the spectra collected before storage 369

and eight weeks postharvest. The distribution in the PCA score plot of fruit spectra acquired 370

before storage showed two distinctive clusters corresponding to two canopy positions. This 371

grouping was only possible on spectra transformed using Savitzky Golay second derivative 372

pre-processing method (Fig. 1A). These clusters allowed distinction between fruit from 373

different canopy positions with accuracy of 84%, i.e. only 8 fruit were misidentified. The first 374

two PCs accounted for 86.0 % of the total variability (Fig. 1A), PC1 explained the 71.0 % of 375

the variance and PC2 explained 15.0 % of the variance. The effective wavelength band for 376

this classification was from 450 to 1200 nm with a strong absorption at 670 nm influenced by 377

chlorophyll, two bands at 740 and 980 nm corresponding to water (O-H) functional groups 378

and one at 1200 nm associated with C-H stretching for carbohydrates. This confirms that a 379

16

combination of colour, carbohydrates and moisture content of the rind play an important role 380

in discriminating between fruit from different positions of the canopy. As previously reported 381

by Magwaza et al. (2012a), PCA models developed using spectra restricted to either the 382

visible range or to NIR range did not show these clusters demonstrating the importance of the 383

combination of visible and NIR range in the classification of fruit by their canopy position. 384

Spectral data collected after eight weeks of postharvest was also subjected to a similar 385

analysis, but clusters were not easily identifiable (Fig. 1B). In this case, the first two PCs 386

accounted for 57.0 % of the total variability, PC1 explained the 42.0 % of the variance and 387

PC2 explained 15.0 % of the variance. Improved rind colour during storage is one of the 388

possible reasons explaining poor classifications and misidentification observed after 8 weeks 389

of storage. 390

391

To further test the potential of Vis/NIR spectral information to discriminate fruit from 392

different canopy positions, spectral data obtained before storage was subjected to PLS-DA by 393

assigning fruit from each canopy positions to a dummy variable (1, and 2 for outside and 394

inside, respectively). Performance of the PLS-DA model to classify fruit based on their origin 395

within the tree canopy using spectral range between 450 and 2400 nm and MSC spectral pre-396

processing is shown in Fig. 3. The results in this study showed that fruit from inside the 397

canopy had higher susceptibility to the disorder than outside fruit. In practical terms, the 398

ability of Vis/NIRS to non-destructively classify fruit based on their origin within the canopy, 399

suggests the potential of this technology to classify individual fruit in a packing line, for 400

either local (inside fruit) or export market and long term storage (outside fruit). 401

402

Due to discrete nature of RBD scores, correlating it with complicated NIR data was difficult 403

and model statistics were poor (R < 0.10). In terms of predicting RBD, obtained calibration 404

17

and validation statistics were very low with RPD of 0.45. There are standards for RPD values 405

where values below 1.0 are statistically unusable (Kuang & Mouazen, 2013). Therefore, the 406

low RPD values clearly indicate poor accuracy of these models. The complexity of biological 407

factors involved in the development of RBD (Cronje et al., 2011a, b, 2013; Magwaza et al., 408

2012a; 2013d) may also account for the difficulty of developing a reliable prediction model 409

for RBD. Another reason for the poor prediction model for RBD is that calibration and test 410

set also contained a large proportion of samples in which RBD did not develop. In future, it 411

will be necessary to increase sample size and use fruit from different localities in order to 412

increase chances of the disorder. This is to ensure that the distribution of the disorder is wide, 413

normal around the mean and evenly distributed along the entire range to avoid the Dunn 414

effect (Williams & Norris, 2001; Davey et al., 2009). 415

416

Due to poor prediction of RBD, a different approach was applied, where samples were 417

assigned a binary dummy variable (0 and 1), which indicated whether the sample belongs to 418

particular RBD group or not. RBD affected fruit were set as reference data 1 while unaffected 419

fruit were assigned to 0. After this analysis, the model statistical parameters for external 420

prediction were improved to R = 0.61, RMSEP = 0.34 and RPD = 0.45. Considering that 421

RBD was a category binary variable ranging from 0 to 1, the RMSEP of 0.34 was too high 422

(Table 3 and Fig. S3). In addition, the small y-variance explained by this model (18%) also 423

proved this model to be poor and not able to accurately predict RBD. The upper limit for 424

PLS-DA discrimination of fruit without RBD was 0.5, and samples with predicted value of 425

0.5 or higher were classified as having RBD, while samples with predicted values higher than 426

0.5 were not affected by the disorder (Fig. S3). The RBD PLS-DA prediction model had low 427

false positive (6.0%) and relatively high false negatives (20.0%) resulting in the overall 428

accuracy of 74.0%. 429

18

430

The prediction performance of the models developed and validated using Vis/NIRS signals 431

acquired from fruit before storage and eight weeks after storage are summarized in Table 3. 432

PLS model based on spectra acquired before storage was better for predicting CI (R = 0.94, 433

RMSEP = 0.38 and RPD = 4.12) than models based on spectra acquired after 8 weeks of 434

storage (R = 0.89, RMSEP = 0.65 and RPD = 2.43). For RBD, a similar trend was observed, 435

where model prediction statistics was better when using spectra acquired before storage 436

compared to spectra after storage. In ‘Nules Clementine’ mandarins, rind colour of fruit 437

harvested from inside and outside the canopy has been previously reported to improve during 438

storage (Magwaza et al., 2013d). The loss of prediction advantage reported in the current 439

study on rind colour could be attributed to the inclusion of the visible waveband in these 440

models. The prediction performance of rind carbohydrates and DM was slightly higher for 441

the models based on spectra acquired after storage compared to those acquired from fruit 442

before storage. An example of this improved trend of model performance with time is shown 443

on rind DM, where the RPD value of the model with spectra obtained before storage was 3.06 444

while that for spectra acquired after 8 weeks of storage was 3.84. This was also evident for 445

sugars. NIRS is expected to perform well to predict sugar concentration as reported before 446

(Golic et al., 2003).

447

448

4. Conclusions 449

450

This study showed the capability of Vis/NIRS coupled with chemometric analysis of spectra 451

acquired from intact fruit at harvest to predict postharvest rind physico-chemical properties 452

related to rind quality and susceptibility to RBD. Due to the discrete nature of RBD scores, 453

correlating it with complex Vis/NIRS data was difficult and PLS model statistics were poor, 454

19

suggesting that the technique is not able to accurately predict the disorder. The complexity of 455

biological factors involved in the development of RBD may also account for the difficulty of 456

developing a reliable prediction model for RBD. Nevertheless, rind physico-chemical 457

properties such as rind sugars and dry matter content, which have been identified as potential 458

biochemical indicators of fruit susceptibility to RBB (Cronje et al., 2011; Magwaza et al., 459

2013d), were predicted with accuracy for up to eight weeks of fruit storage. Taking into 460

account our previous studies shown that rind carbohydrates concentration and DM can be 461

used as biochemical markers for fruit susceptibility to RBD, the ability of Vis/NIRS to 462

predict these parameters, revealed the capability of Vis/NIRS and chemometrics to predict 463

postharvest behaviour of mandarins and hence susceptibility to RBD disorder. However, 464

there is still no definite upper or lower limit of carbohydrates concentration or DM in which 465

the disorder occurs or does not occur. Hence, further research still needs to be conducted to 466

explain whether Vis/NIR-predicted carbohydrates concentration is useful for determining 467

fruit susceptibility to postharvest RBD. PCA and PLS-DA models based on spectra acquired 468

before harvest were able to discriminate fruit based on their position within the canopy. 469

Given that fruit located inside the canopy are more susceptible to RBD, the accuracy of the 470

two regression methods demonstrated that both methods could be used, individually or in 471

combination, for screening between inside and outside fruit and discriminate fruit based on 472

susceptibility to RBD. This information could be used as an on-line deciding tool, during 473

packing, to decide on fruit destined for long distance export market (outside) and those 474

destined for short distance or local market (inside fruit). 475

476

Acknowledgements 477

478

20

This work is supported by the South African Research Chairs Initiative of the Department of 479

Science and Technology and National Research Foundation. The authors are grateful to the 480

South African Perishable Products Export Control Board (PPECB) and the South 481

Africa/Flanders Research Cooperation Programme (Project UID: 73936) for financial support 482

which made it possible to undertake the study, and to Ms Rosemary Burns, Dr Katherine 483

Cools, Dr Ma Carmen Alamar for technical research support. Dr Lembe Magwaza’s study 484

visit at Cranfield University was partly funded by the Commonwealth Scholarship 485

Commission of the United Kingdom. 486

487

References 488

489

Alférez, F., & Burns. J. (2004). Postharvest peel pitting at non-chilling temperatures in 490

grapefruit is promoted by changes from low to high relative humidity during storage. 491

Postharvest Biology and Technology, 32, 79–87. 492

Alvarez-Guerra, M., Ballabio, D., Amigo, J. M., Bro, R., & Viguri, J. R. (2010). 493

Development of models for predicting toxicity from sediment chemistry by partial least 494

squares-discriminant analysis and counter-propagation artificial neural networks. 495

Environmental Pollution, 158, 607–614. 496

Antonucci, F., Pallottino, F., Paglia, G., Palma, A., D’Aquino, S., & Menesatti, P. (2011). 497

Non-destructive estimation of mandarin maturity status through portable VIS-NIR 498

spectrophotometer. Food and Bioprocess Technology, 4, 809–813. 499

Blasco, J., Aleixos, N., & Moltó. E. (2007). Computer vision detection of peel defects in 500

citrus by means of a region oriented segmentation algorithm. Journal of Food 501

Engineering, 81, 535–543. 502

21

Chen, X., Wu, D., He, Y., & Liu, S. (2011). Nondestructive differentiation of Panax species 503

using visible and shortwave near-infrared spectroscopy. Food and Bioprocess 504

Technology, 4, 753–761. 505

Clément, A., Dorais, M., & Vernon, M. (2008). Nondestructive measurement of fresh tomato 506

lycopene content and other physicochemical characteristics using visible-NIR 507

spectroscopy. Journal of Agricultural and Food Chemistry, 56, 9813–9818. 508

Cronje, P. J. R., Barry, G. H., & Huysamer, M. (2011). Postharvest rind breakdown of ‘Nules 509

Clementine’ mandarin is influenced by ethylene application, storage temperature and 510

storage duration. Postharvest Biology and Technology, 60, 192–201. 511

Cronje, P. J. R., Barry, G. H., & Huysamer, M., 2013. Canopy position affects pigment 512

expression and accumulation of flavedo carbohydrates of ‘Nules Clementine’ mandarin 513

fruit, thereby affecting rind condition. Journal of the American Society for Horticultural 514

Science, 138, 217–244. 515

Davey, M. W., Saeys, W., Hof, E., Ramon, H., Swennen, R.L., & Keulemans, J. (2009). 516

Application of visible and near-infrared reflectance spectroscopy (Vis/NIRS) to 517

determine carotenoid contents in banana (Musa spp.) fruit pulp. Journal of Agricultural 518

and Food Chemistry, 57, 1742–1751. 519

Escuredo, O, Seijo, M.C., Salvador, J., & González-Martín, M. I. (2013). Near infrared 520

spectroscopy for prediction of antioxidant compounds in the honey. Food Chemistry, 521

141, 3409–3414. 522

Golic, M., Walsh, K. W., & Lawson, P. (2003). Short-wavelength near-infrared spectra of 523

sucrose, glucose, and fructose with respect to sugar concentration and temperature. 524

Applied Spectroscopy, 57, 139–145. 525

22

Gómez, A. H., He, Y., & Pereira, A. G. (2006). Non-destructive measurement of acidity, 526

soluble solids and firmness of Satsuma mandarin using Vis-NIR spectroscopy 527

techniques. Journal of Food Engineering, 77, 313–319. 528

Gómez-Sanchis, J., Lorente, D., Soria-Olivas, E., Aleixos, N., Cubero, S., & Blasco, J. 529

(2013). Development of a hyperspectral computer vision system based on two liquid 530

crystal tuneable filters for fruit inspection: application to detect citrus fruits decay. Food 531

and Bioprocess Technology, Doi:10.1007/s11947-013-1158-9. 532

Kawano, S., Fujiwara, T., & Iwamoto, M. (1993). Non-destructive determination of sugar 533

content in ‘Satsuma’ mandarins using NIRS transmittance. Journal of the Japanese 534

Society for Horticultural Science, 62, 465–470. 535

Kuang, B., & Mouazen, A. M. (2011). Calibration of visible and near infrared spectroscopy 536

for soil analysis at the field scale on three European farms. European Journal of Soil 537

Science, 62, 629–636. 538

Kuang, B., & Mouazen, A. M. (2013). Non-biased prediction of soil organic carbon and total 539

nitrogen with vis–NIR spectroscopy, as affected by soil moisture content and texture. 540

Biosystems Engineering, 114, 249–258. 541

Lammertyn, J., Peirs, J., De Baerdemaeker, J., & Nicolaï, B. M. (2000). Light penetration 542

properties of NIR radiation in fruit with respect to non-destructive quality assessment. 543

Postharvest Biology and Technology, 18, 121–132. 544

Magwaza, L. S., Opara, U. L., Terry, L. A., Landahl, S., Cronje, P. J. R., Nieuwoudt, H., 545

Mouazen, A. M., Saeys, W., & Nicolai B. M. (2012a). Prediction of ‘Nules Clementine’ 546

mandarin susceptibility to rind breakdown disorder using Vis/NIR spectroscopy. 547

Postharvest Biology and Technology, 74, 1–10. 548

23

Magwaza, L. S., Opara, U. L., Nieuwoudt, H., Cronje, P. J. R., Saeys, W., & Nicolaï, B. 549

(2012b). NIR spectroscopy applications for internal and external quality analysis of 550

citrus fruit – a review. Food and Bioprocess Technology, 5, 425–444. 551

Magwaza, L. S., Ford, H. D., Cronje, P. J. R., Opara, U. L., Landahl, S., Tatam, R. P., Terry, 552

L. A. (2013a). Application of optical coherence tomography to non-destructively 553

characterise rind breakdown disorder of ‘Nules Clementine’ mandarins. Postharvest 554

Biology and Technology, 84, 16–21. 555

Magwaza, L. S., Opara, U. L., Cronje, P. J. R., Nicolaï, B. M., Landahl, S., & Terry, L. A. 556

(2013b). Non-chilling physiological rind disorders in citrus fruit. Horticultural Reviews, 557

41, 131–176. 558

Magwaza, L. S., Opara, U. L., Terry, L. A., Landahl, S., Cronje, P. J. R., Nieuwoudt, H. H., 559

Hanssens, A., Saeys, W., Nicolaï, B. M. (2013c). Evaluation of Fourier transform-NIR 560

spectroscopy for integrated external and internal quality assessment of Valencia oranges. 561

Journal of Food Composition and Analysis, 31, 144–154. 562

Magwaza, L. S., Opara, U. L., Cronje, P. J. R., Landahl, S., & Terry, L. A. (2013d). Canopy 563

position effect on rind biochemical profile of ‘Nules Clementine’ mandarin fruit during 564

postharvest storage. Postharvest Biology and Technology, 86, 300–308. 565

Magwaza, L. S., Opara, U. L., Cronje, P. J. R., Nieuwoudt, H. H., Landahl, S., & Terry, L. A. 566

(2013e). Quantifying the effects of fruit position in the canopy on physical and 567

biochemical properties and predicting susceptibility to rind breakdown disorder of ‘Nules 568

Clementine’ mandarin (Citrus reticulata Blanco) using Vis/NIR spectroscopy. Acta 569

Horticulturae, (In Press). 570

Nicolaï, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I., & Lammertyn, 571

J. (2007). Non-destructive measurement of fruit and vegetable quality by means of NIR 572

spectroscopy: A review. Postharvest Biology and Technology, 46, 99–118. 573

24

Pathare, P. B., Opara, U. L., & Al-Said, F. A. (2013). Colour measurement and analysis in 574

fresh and processed foods: a review. Food and Bioprocess Technology, 7, 36–60. 575

Peiris, K. H. S., Dull, G. G., Leffler, R. G., & Kays, S. J. (1999). Spatial variability of soluble 576

solids or dry-matter content within individual fruits, bulbs, or tubers: Implications for the 577

development and use of NIR spectrometric techniques. HortScience, 34, 114–118. 578

Tewari, J. C., Dixit, V., Chi, B-K., & Malik, K. A. (2008). Determination of origin and sugars 579

of citrus fruit using genetic algorithm, correspondence analysis and partial least square 580

combined with fiber optic NIR spectroscopy. Spectrochimica Acta A. Molecular and 581

Biomolecular Spectroscopy, 71, 1119–1127. 582

Wang, W., & Paliwal, J. (2007). Near-infrared spectroscopy and imaging in food quality and 583

safety. Sensing and Instrumentation for Food Quality and Safety, 1, 193–207. 584

Williams, P. C., & Sobering, D. C. (1996). “How do we do it: a brief summary of the 585

methods we use in developing near infrared calibrations”. In Proceedings of the 7th 586

International Conference of Near Infrared Spectroscopy: Near Infrared Spectroscopy: 587

The future waves, Davies A. M. C. & Williams P. eds. Montreal, Chichester, UK. Pp. 588

185-188. 589

Williams, P., Norris, K.H., 2001. Variable affecting near infrared spectroscopic analysis. In: 590

Williams, P., Norris, K.H. (eds). Near infrared technology in the agriculture and food 591

industries, 2nd edition. The American Association of Cereal Chemists, St Paul, MNL. 592

Pp. 171–185 593

Xudong, S., Hailiang, Z., & Yande, L. (2009). Nondestructive assessment of quality of 594

‘Nanfeng’ mandarin fruit by a portable near infrared spectroscopy. International Journal 595

of Agricultural and Biological Engineering, 2, 65–71. 596

25

Zheng, Y., He, S., Yi, S., Zhou, Z., Mao, S., Zhao, X., & Deng, L. (2010). Predicting 597

oleocellosis sensitivity in citrus using Vis-NIR reflectance spectroscopy. Scientia 598

Horticulturae, 125, 401–405. 599

600

26

601

602

603

604

605

606

607

608

609

610

611

612

613

614

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617

618

619

620

621

622

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Fig. 1: 624

625

PC1 (71%)

PC1 (42%)

PC

2 (

15%

) P

C2 (

15%

) A

B

27

626

Fig. 2: 627

628

629

Fig. 3: 630

631

Figure captions: 632 633

634

Fig. 1: Principal component (PC) analysis (PCA) score plot showing the ability of 635

spectra acquired before storage (A) and after 8 weeks of postharvest storage (B), to sort 636

fruit based on their origin within the tree canopy. 1 and 2 represent fruit from outside 637

and inside the canopy, respectively. 638

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 2 4 6 8 10 12 14 16 18 20

Y-v

ari

ance

Number of latent variables

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

35

0

55

0

75

0

95

0

11

50

13

50

15

50

17

50

19

50

21

50

23

50

Reg

ress

ion

coef

fici

ents

Wavelength (nm)

y = 0.92x + 0.11

Rv = 0.96

0.5

1.0

1.5

2.0

2.5

0.5 1.0 1.5 2.0 2.5

Vis

/NIR

pre

dic

ted p

osi

tio

n

Actual position within the canopy

A B

28

639

Fig. 2: Residual y-variance as a function of the number of latent variables (LVs) in the 640

dry matter (DM) model (A), and regression coefficients curve of the DM model of intact 641

‘Nules Clementine’ mandarin fruit with 8 LVs and NIR spectral range of 450–2450 nm 642

(B). 643

644

Fig. 3: PLS-DA models showing the capability of Vis/NIR spectral analysis in predicting 645

fruit origin within the tree canopy. Spectra for this model were acquired before storage. 646

647

29

Table 1: Mean, standard deviation (SD), range and coefficient of variation (CV%) for 648

calibration (n = 100) and independent validation (n = 50) subsets of mandarin fruit physico-649

chemical properties. 650

Quality parameter Calibration data set Validation data set

Mean±SD Range CV% Mean±SD Range CV%

Colour index 4.38±1.66 0.07-7.49 37.81

4.42±1.58 0.07-7.43 35.75

Rind dry matter (%) 28.42±3.07 22.16-35.41 10.82

28.12±3.19 22.84-35.46 11.34

Rind fructose (mg/g DW) 120.12±34.70 53.72-195.88 28.88

120.58±35.09 64.826-195.88 29.10

Rind glucose (mg/g DW) 81.20±30.59 22.19-152.15 37.68

80.70±31.09 36.01-152.16 38.53

Rind sucrose (mg/g DW) 88.19±41.63 35.85-207.95 47.32

92.68±42.73 35.90-206.30 44.47

Rind total carbohydrates (mg/g DW) 289.50±99.14 122.03-501.12 34.25 293.95±94.95 137.66-512.01 32.30

30

Table 2: Results for calibration and prediction of the partial least squares (PLS) models 651

developed and validated using different spectral data pre-processing methods. 652

Quality Calibration Validation Info. Regionn

parameter Pre.Prb LV

h Rc

i RMSEC

j Slope Rv

k RMSEP

l RPD

m Slope (nm)

Colour index Nonec 2 0.95 0.37 0.95 0.94 0.38 4.12 0.94 450-750

SNVd 2 0.94 0.39 0.94 0.94 0.39 4.02 0.93 450-750

1st der

e 2 0.90 0.49 0.90 0.88 0.62 2.56 0.90 450-750

2nd

derf 2 0.90 0.49 0.90 0.88 0.62 2.56 0.88 450-750

MSCg 2 0.95 0.37 0.94 0.94 0.44 3.60 0.93 450-750

Rind DMa None 8 0.95 0.73 0.95 0.91 1.24 2.56 0.96 900-1700

(%) SNV 8 0.91 0.93 0.91 0.91 0.99 3.23 0.91 900-1700

1st der 8 0.96 0.64 0.96 0.92 0.92 3.47 0.93 900-1700

2nd

der 8 0.84 1.28 0.83 0.83 1.35 2.37 0.83 900-1700

MSC 8 0.93 0.68 0.95 0.94 0.94 3.39 0.92 900-1700

Rind fructose None 14 0.87 13.21 0.87 0.85 13.60 2.58 0.85 900-1700

(mg/g DW) SNV 14 0.88 11.96 0.88 0.82 18.20 1.93 0.79 900-1700

1st der 14 0.99 3.99 0.99 0.89 18.43 1.90 0.92 900-1700

2nd

der 14 0.84 14.14 0.84 0.73 23.34 1.50 0.77 900-1700 MSC 14 0.91 9.18 0.90 0.90 11.41 3.08 0.88 900-1700

Rind glucose None 10 0.84 12.53 0.84 0.81 14.90 2.09 0.81 900-1700

(mg/g DW) SNV 10 0.54 20.45 0.54 0.62 16.98 1.83 0.58 900-1700

1st der 10 0.97 5.02 0.98 0.48 20.57 1.51 0.57 900-1700

2nd

der 10 0.69 16.73 0.69 0.59 24.31 1.28 0.62 900-1700

MSC 10 0.89 10.32 0.88 0.88 14.19 2.19 0.86 900-1700

Rind sucrose None 10 0.57 27.46 0.57 0.45 36.81 1.12 0.59 900-1700

(mg/g DW) SNV 10 0.49 29.36 0.49 0.42 34.18 1.21 0.43 900-1700

1st der 10 0.79 18.22 0.79 0.77 25.38 1.62 0.77 900-1700

2nd

der 10 0.72 24.11 0.72 0.63 28.62 1.44 0.71 900-1700

MSC 10 0.92 12.37 0.92 0.83 24.36 1.75 0.90 900-1700

Rind total None 10 0.93 26.06 0.93 0.83 60.03 1.58 0.95 900-1700

carbohydrates SNV 10 0.62 60.42 0.62 0.65 48.70 1.95 0.62 900-1700

(mg/g DW) 1st der 10 0.79 42.71 0.79 0.78 47.29 2.01 0.78 900-1700

2nd

der 10 0.93 26.83 0.93 0.86 47.64 1.99 0.88 900-1700

MSC 10 0.89 30.42 0.89 0.90 31.04 3.06 0.91 900-1700

aDM rind dry matter, b

Pre.Pr pre-processing method, cNone no spectral pre-processing, d

SVN standard vector normalization, e1

st der first 653

derivative, f2

nd der second derivative, g

MSC multiple scatter correction, hLV latent variables, i

Rc correlation coefficient for calibration, 654 jRMSEC root mean square error of calibration, kRv correlation coefficient for validation, lRMESP root mean square error of prediction, mRPD 655

residual predictive deviation, nInfo region, informative region of the spectrum. 656

31

Table 3: Model performance using spectra acquired before storage (week 0) and after 8 weeks of 657

storage. 658

Quality Calibration Validation

parameter Time Pre.Prb LVf Rcg RMSECh Slope Rv

i RMSEPj RPDk Slope

Colour index Week 0 Nonec 2 0.95 0.37 0.95

0.94 0.38 4.12 0.94

Week 8

2 0.91 0.63 0.93

0.89 0.65 2.43 0.90

RBD (binary scores) Week 0 2nd derd 5 0.79 0.27 0.67

0.70 0.34 0.45 0.45

Week 8

5 0.77 0.29 0.59

0.61 0.36 0.42 0.45

Rind DMa (%) Week 0 MSCe 8 0.93 0.94 0.89

0.90 1.04 3.06 0.88

Week 8

8 0.96 0.64 0.96

0.94 0.83 3.84 0.93

Rind fructose (mg/g DW) Week 0 MSC 14 0.91 9.18 0.90

0.90 11.41 3.07 0.88

Week 8

14 0.96 6.33 0.97

0.94 10.01 3.51 0.97

Rind glucose (mg/g DW) Week 0 MSC 10 0.89 10.32 0.88

0.88 14.19 2.19 0.86

Week 8

10 0.91 9.18 0.90

0.90 11.41 2.72 0.88

Rind sucrose (mg/g DW) Week 0 MSC 8 0.89 13.44 0.91

0.75 38.21 1.07 0.59

Week 8

8 0.92 12.38 0.92

0.83 24.36 1.69 0.90

Rind total sugars (mg/g DW) Week 0 MSC 10 0.89 30.42 0.89 0.87 31.35 3.03 0.87 Week 8 10 0.93 25.35 0.91

0.88 31.04 3.05 0.91 aDM rind dry matter, b

Pre.Pr pre-processing method, cNone no spectral pre-processing,d

2nd

der second derivative, eMSC multiple scatter 659

correction, fLV latent variables, g

Rc correlation coefficient for calibration, hRMSEC root mean square error of calibration, i

Rv correlation 660

coefficient for validation, jRMESP root mean square error of prediction, kRPD residual predictive deviation. 661

662

663

32

Highlights 664

665

• Fruit from inside the canopy were more susceptible to RBD disorder than outside fruit 666

• Vis/NIRS PLS models predicted rind fructose, glucose and sucrose with accuracy 667

• Vis/NIRS PCA model was able to non-destructively classify fruit based on tree 668

canopy 669

• Models based on pre-storage spectra gave better prediction of RBD than post-storage 670

671