identification of concealed damage in raw almonds (Prunus ... · 42 Introduction 43 44 Concealed damage (CD) in raw almonds (Prunus dulcis (Mill.)D.A. Webb) is defined by 45 the industry

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Article

Use of near-infrared spectroscopy and chemometrics for the non-destructiveidentification of concealed damage in raw almonds (Prunus dulcis)

Cristian Rogel-Castillo, Roger B. Boulton, ArunwongOpastpongkarn, Guangwei Huang, and Alyson Elayne Mitchell

J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b01828 • Publication Date (Web): 16 Jun 2016

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Use of near-infrared spectroscopy and chemometrics for the non-destructive identification 1

of concealed damage in raw almonds (Prunus dulcis) 2

3

Cristian Rogel-Castillo1, Roger Boulton

2, Arunwong Opastpongkarn

1, Guangwei Huang

3 and 4

Alyson E. Mitchell1* 5

6

1Department of Food Science and Technology, University of California Davis, One Shields 7

Avenue, Davis, CA 95616, United States; 2 Department of Viticulture and Enology, University 8

of California Davis, One Shields Avenue, Davis, CA 95616, United States; 3

Almond Board of 9

California, 1150 9th Street, Suite 1500, Modesto, CA 95354 United States; 10

11

*Address correspondence to this author at Department of Food Science and Technology, 12

University of California, Davis, CA 95616 [telephone (530) 304-6618; fax (530) 752-4759; e-13

mail [email protected]] 14

15

16

17

18

19

20

Page 1 of 24

ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

Abstract 21

22

Concealed damage (CD) is defined as a brown discoloration of the kernel interior 23

(nutmeat) that appears only after moderate to high heat treatment (e.g. blanching, drying, 24

roasting, etc.). Raw almonds with CD have no visible defects before heat treatment. Currently 25

there are no screening methods available for detecting CD in raw almonds. Herein, the feasibility 26

of using Near-Infrared Spectroscopy (NIR) between 1125–2153 nm for the detection of CD in 27

almonds is demonstrated. Almond kernels with CD have less NIR absorbance in the region 28

related with oil, protein and carbohydrates. Using partial least square regression and discriminant 29

analysis (PLS-DA) and by selecting specific wavelengths, three classification models were 30

developed. The calibration models have false positive and false negative error rates ranging 31

between 12.4-16.1% and 10.6-17.2%, respectively. The percent error rates ranged between 8.2-32

9.2%. Second derivative pre-processing of selected wavelength resulted in the most robust 33

predictive model. 34

35

Keywords 36

37

Prunus dulcis; Almond, Concealed Damage; Near Infrared Spectroscopy; Partial Least Square 38

Regression; Discriminant Analysis. 39

40

41

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Introduction 42

43

Concealed damage (CD) in raw almonds (Prunus dulcis (Mill.) D.A. Webb) is defined by 44

the industry as a brown discoloration of the kernel interior (nutmeat) that appears only after 45

moderate to high heat treatment (e.g. blanching, drying, roasting, etc.,) as shown in Figure 1. CD 46

may develop anytime during harvest when rain occurs, or after harvest when kernels are in 47

windrows or stockpiles and exposed to warm and moist environments.1,2

Raw almond kernels 48

with CD, have no visible defects on the interior or exterior surface of the kernel. Additionally, 49

there are no visible signs of CD on the surface of whole roasted kernels.3 CD is frequently 50

associated with a strong bitter flavor(s) that can result in immediate consumer rejection.1 51

Currently there are no screening methods available for detecting CD in raw almonds, or other 52

nuts affected by CD, and processors often do not realize nuts are damaged until after they have 53

been roasted.1 Under current production practices, the most common methods for detecting CD 54

involve visual inspection of roasted almonds after they are split open. Kernels with a “dark 55

brown” color over ~50% of the interior of the kernel are considered to have CD.4 A similar 56

approach is used for hazelnuts.5 Visual inspection and manual sorting is time-consuming, 57

subjective, labor intensive and cannot be used to identify nuts with CD before heat treatments. 58

This can result in significant product loss. 59

The current hypothesis is that the browning associated with CD is related to the Maillard 60

reaction. Moisture can induced the hydrolysis of carbohydrates and potential availability of 61

reducing sugars for Maillard browning reactions. For example, in macadamia nuts exposed to 62

moisture during harvesting, increased levels of reducing sugars were observed in nuts with 63

internal browning.6 Similar observations were made in hazelnuts

7 and in almonds exposed to 64

Page 3 of 24



simulated rainfall.2 In more recent studies, elevated levels of volatiles related to lipid oxidation 65

and amino acid degradation were observed in almonds with CD.8 Both lipid oxidation products 66

and protein degradation products can serve as reactants in the Maillard browning reaction. 67

Near-infrared spectroscopy (NIR) is a rapid and effective method for screening foods for 68

specific chemical and physical characteristics.9 NIR is advantageous as a screening method as it 69

is non-destructive, can be used on whole foods, and produces no waste. The NIR spectral region 70

(720 to 2500 nm) is ideally suited for foods as it contains absorbance bands that result primarily 71

from three chemical bonds: C-H (fats, oil, hydrocarbons), O-H (water, alcohol) and N-H 72

(protein). NIR spectroscopy is increasingly considered one of the more promising in-line 73

detection methods for rapidly measuring specific chemical properties of food.6 It has been 74

successfully applied in detecting quality defects in macadamia kernels7, walnuts

10, chestnut

11 - 12, 75

hazelnuts13 - 14

, and soybean seed.15

It has also been employed for food composition analysis 76

including oleic and linoleic acid content in peanut seed16

, acidity and water content in 77

hazelnuts17

, and characterization of shea tree nut fat profiles.18

78

Pearson (1999) was the first to recognize the use of NIR spectroscopy for the 79

identification of CD in raw almonds4, 19

and evaluated the transmission spectrum from 700–1400 80

nm in almonds soaked in water, and dried but not roasted. In these studies, almonds were either 81

soaked for 30 minutes and exposed to 95% relative humidity for 30 hours (short moisture), or 82

soaked for 60 minutes and exposed to 95% relative humidity for 60 hours (long moisture). 83

Almonds were then dried at either 55 or 110°C. The higher temperature and shorter soak times 84

produced the greatest amount of CD. Almonds with CD had enhanced absorption at 930 nm (oil 85

absorption band). Raw almonds with CD could be distinguished from normal almonds at an error 86

of 12.4% by using principal components of the absorbance, first derivative and second derivative 87

Page 4 of 24



spectra between 1000-1300 nm. Pearson (1999) recognized that collecting the NIR spectra over 88

the full transmission range would be too slow to achieve desired inspection rates of 40 nuts/s and 89

therefore tested the feasibility of using just 6 light emitting diodes at 660, 830, 880, 890, 940, 90

and 950 nm19

. These data resulted in a classification error rate of 14.3% for the validation set. 91

More recently, Nakariyakul20 - 21

achieve a higher classification rate using hyperspectral 92

transmission and focusing on a sub-set of absorbing bands (760, 920, 935, and 970 nm) with a 93

false negative error rate of 14.81%. Almonds used in this study were generated by Pearson 94

(1999) as described above. 95

Herein, we present the development of a prediction model for the classification of 96

almonds with CD using reflectance NIR in the extended range of the NIR spectrum (1125 – 2153 97

nm) and by employing data pre-processing and partial least square discriminant analysis (PLS-98

DA). Almonds evaluated in this study were exposed to controlled humidity environments that 99

produced an internal nut moisture content of ~5% (control), 8% (mild CD) and 11% (100% CD). 100

The percent CD in the raw almonds was validated using colorimetry as described previously8. 101

Developing a rapid in-line screening method for detecting CD in raw almonds is a critical 102

step towards improving quality control measures in almond processing and offers the advantage 103

of sorting almonds with CD into product lines that do not require roasting or other heat 104

treatments. 105

106

107

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Materials and Methods 108

109

Sample preparation 110

Dehulled raw kernels (100 lbs., var. Nonpareil) were supplied by the Nickels Soil lab 111

(Arbuckle, CA) in September 2013. Individual vessels containing ~100 gm were exposed to 112

conditions that produced an internal kernel moisture of 5% (actual 5.4 ± 0.2), 8% (actual 8.6 ± 113

0.7) or 11% (actual 10.4 ± 1.5) in a controlled atmosphere (Thermo Scientific, Marietta, OH) at 114

45 ± 2°C. Under these conditions, CD is observed after 24 hours. The moisture content of the 115

almonds was validated gravimetrically by drying samples (~1 g) at 95-105°C under vacuum to a 116

constant weight. Moisture was determined in triplicate, and the results were averaged. 117

118

Near-infrared reflectance spectra measurement 119

NIR diffuse reflectance spectra were measured on single whole raw almond kernels using 120

an extended MicroNIR 2200 spectrometer (JDSU, USA). The spectral range was collected from 121

1125 – 2153 nm using sampling intervals of 8 nm per pixel. The detector used was a 128 pixel 122

uncooled element InGaAs (JDSU, USA). Reflectance spectra data (R) were converted to 123

absorbance using the log (1/R) transformation. A Spectralon® SRM-99 Diffuse Reflectance 124

Standard (Labsphere®, New Hampshire, USA) was used as white calibration reference. For each 125

spectrum, 1000 scans with an integration time of 550 µs were averaged. 126

127

Data Pre-processing 128

Page 6 of 24



NIR spectra are complex with broad overlapping NIR absorption bands making it often 129

difficult to identify unique spectral features related to individual chemical components within a 130

given sample. Therefore, a mathematical treatment (pre-processing) of NIR spectra is often used 131

to correct for unwanted systematic sample-to-sample variation (e.g., kernel shape, roughness of 132

kernel surface); help remove spectral baseline shift and scattering caused by particle size 133

differences; reduce band overlapping; and enhance spectral differences.22

Data pre-processing 134

results in relevant NIR spectral data extraction without losing information while removing 135

unwanted information (e.g., interferences or noise).22

136

After the acquisition, NIR spectra were converted to absorbance and pre-processed using 137

either standard normal variate (SNV) or a 9- point second order Savitzky-Golay filter (second 138

derivative preprocessing). These two techniques alone and a combination were compared to 139

determine their effectiveness at removing baseline offsets. Data pre-processing was performed 140

using R and R-Studio (version 0.98.1102). The following packages were used for preprocessing 141

and PLS-DA: ChemometricsWithR23

, signal24

, plyr25

, dplyr26

and caret27

. 142

143

Determination of CD by colorimetry 144

After NIR spectra were acquired, the almonds were roasted at 120°C for 90 min in a 145

convection oven (Thermo Scientific, USA). Almonds were then split in half along the natural 146

seam and the color of the internal kernel was measured using a ColorFlex colorimeter 147

(HunterLab, USA) according to methods established previously8. The color values L* 148

(lightness), C (Chroma), and h (hue), according to the CIE LCh color scale were recorded using 149

a portsize of 0.4 inches with D65 optical sensor, 0° geometry and 10° angle of vision. Almonds 150

Page 7 of 24



with CD (L* ≤ 71) were identified and grouped separately from those with no concealed damage 151

(NCD; L* > 71).8 152

153

Prediction of almonds with concealed damage 154

Pre-processed spectra (SNV, second derivative and a combination of both) were analyzed 155

using partial least squares discriminant analysis (PLS-DA). Almonds were separated into two 156

groups (NCD and CD) using colorimetry. The data set (855 almonds) was then randomly divided 157

into a calibration (655 almonds) and a validation (200 almonds) sample set. NCD and CD 158

almonds were assigned constant values of 0 and 1 for a two-class model, respectively. 159

For the calibration model, repeated cross-validation was used to find the best model. 160

Calibration models were evaluated based on the percentage false positive (% fp), percentage 161

false negative (% fn) and percentage error rate (% ER). A fn was defined as the percentage of 162

NCD almonds classified as those with CD, while fp was defined as CD almonds classified as 163

NCD. The % ER represents the percentage of total almonds incorrectly classified by the 164

predictive method.4, 12

165

166

Results and Discussion 167

168

A representative NIR spectrum (1125-2153 nm) of the NCD almonds, after SNV pre-169

processing, is shown in Fig. 2. The spectrum is characterized by broad and unresolved absorption 170

bands and is similar to spectra for peanut28

, walnut10

, macadamiat7, and shea nut.

18 To enhance 171

Page 8 of 24



spectral features and compensate for baseline offsets, a second-derivative of the absorbance data, 172

with respect to wavelength, was calculated. In the second-derivative data, absorbance maxima 173

are converted to minima (Fig. 3). The NIR spectra obtained after applying the second-derivative 174

were characterized by 10 absorption bands. These bands correlate with the major constituents of 175

raw almonds: lipid (50%), carbohydrates (∼22%), and protein (∼21%).29

The absorption bands 176

between 1165-1238 nm, 1692-1740 nm and 2064-2104 nm are associated with lipids. These 177

include the C-H (-CH) second overtone stretching band (1200-1214 nm)30

, the C-H (-CH2) first 178

overtone stretching band (1700-1724 nm)6, 31

and C-H combination band (~2098 nm).32

The 179

absorption bands in between 1408-1462 nm and 1902-1959 nm are associated with the H-OH 180

second overtone of water18

as well with protein. The absorption bands between 1692-1740 nm 181

and 2064-2104 nm are associated with the absorption of protein (∼1700–1850 nm) and amino 182

acids (∼2080 nm) respectively32

and the region between 1902-1959 nm correlate to water and 183

amides (∼1910–1920 nm).32 Additionally, the absorption band between 2064-2104 nm can be 184

associated with the O-H and the carboxylic group (C=O-O) band of carbohydrates.31

185

An overlay of the averaged second derivative spectra for almonds classified as NCD and 186

CD is also given in Fig. 3. The main differences between the NCD and CD spectra occur at 187

1432, 1457, 1505, 1513, 1708, 1918 and 2080, 2096 nm. The absorption bands at 1432, 1457, 188

1505, 1513, and 1918 nm correspond to protein33

, the band at 1708 nm corresponds to free fatty 189

acids and oil12, 33

, and absorption at 2080 and 2096 nm correspond to carbohydrates. Almonds 190

with CD present less absorbance in these regions indicating that kernels display decreased levels 191

of lipids, protein and carbohydrates as compared to controls. These results corresponds to 192

observations of King et al.34

(1983) who reported that almonds with CD have lower crude fat 193

(oil) and total carbohydrates as compared to almonds with no concealed damage. Additionally, 194

Page 9 of 24



we recently demonstrated higher levels of volatiles related to lipid oxidation and amino acid 195

degradation in almonds with CD as compared to almonds with no concealed damage.8

Taken 196

together these results indicate the metabolic processes that activate the degradation of proteins, 197

carbohydrates and lipids are involved in the development of CD. The free amino acids, sugars 198

and products from the oxidation of lipids would be substrates for the Maillard reaction and 199

supports the hypothesis that the Maillard reaction is involved in the formation of CD in almonds. 200

Initially, multiple PLS-DA models were evaluated using the full wavelength region from 201

1125-2153 nm after data pre-processing (SNV, second derivative, and SNV and the second 202

derivative). In general, the best predictive models give low percentage error rates (i.e. the highest 203

percentage of correct classification). Herein we found that using the full wavelength region 204

resulted in models with high percentage error rates and therefore PLS-DA models were 205

developed using only relevant portions of the NIR spectra. Table 1 summarize the prediction 206

performance of the calibration models and validation models, which were selected as they had 207

the lowest percent of error (% ER), false positive (% fp) and false negative (% fn) rates. A large 208

data set (200 samples) was analyzed to optimize the prediction models. The lowest % ER (8.2%) 209

was obtained using only second derivative pre-processing as compared to 9.2% when using SNV 210

pre-processing, and 8.2% when using SNV and second derivative pre-processing. Although the 211

% fp rate was higher for this model (17.2%) as compared to the SNV (12.6%) and SNV and 212

second derivative pre-processing (10.6%), the % fn was significantly lower (12.4%) as compared 213

to these models (16%). 214

Previous studies employing IR to build models to discriminate differences between CD 215

and NCD focused on the absorbance range between 700-1400 nm5 and on selected wavelengths 216

within the 700-1400 nm absorbance range.20 - 21

Comparisons of these results with results 217

Page 10 of 24



obtained herein are summarized in Table 2. Although the % fp were lower across these studies 218

(0.7-5.4%), as compared with our results (12.4 – 16.1%), the rate of % fn were significantly 219

higher (11.1-62.96%) than those obtained using our predictive models (10.6 – 17.2%). 220

Additionally, the % ER ranged from 5.8–27.5% whereas our predictive models gave a much 221

narrower range of 8.2 – 9.2%. The three PLS-DA models presented herein offer significant 222

improvements in the prediction capabilities and are able to identify almonds with CD with 90.8 – 223

91.8% certainty based upon calibration models. Although any of the three models presented 224

could be considered for further development of a rapid in-line screening method for detecting 225

CD in raw almonds, the PLS-DA model based on the second derivative spectra and utilizing four 226

wavelength ranges (i.e.1408–1462, 1692–1740, 1902–1959 and 2064-2104 nm) gives the lowest 227

rate of false negatives and may be the best choice for further method development. 228

Our results indicate that these PLS-DA predictive models offer advantages over 229

previously reported models and that CD is related to the degradation of lipids, carbohydrates and 230

proteins in almonds. 231

232

233

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REFERENCE 234

235

(1) Reil, W, J.M. Labavitch, D. Holmberg. 1996. Harvesting. In Almond Production 236

Manual. W.C. Micke, editor. University of California, Division of Agriculture and 237

Natural Resources (publication 3364). Oakland, CA. pp. 260-264. 238

(2) Kader, A.A. and J.F. Thompson. 1992. Postharvest handling systems: Tree nuts in 239

Postharvest Technology of Horticultural Crops. A.A. Kader, editor. University of California, 240

Division of Agriculture and Natural Resources, publication 3311. pp. 254. 241

(3) Halbrook, W. U; Fuller, G.; Whitehand, L. C. Almond Nutmeat Moisture and Water 242

Activity and its Influence on Fungal Flora and Seed Composition. J. Food Sci. 1983, 48, 615-243

617. 244

(4) Pearson, T. C. Spectral Properties and Effect of Drying Temperature on Almonds with 245

Concealed Damage. LWT--Food Sci. Technol. 1999, 32, 67-72. 246

(5) Pannico, A.; Schouten, R. E.; Basile, B.; Romano, R.; Woltering, E. J.; Cirillo, C. Non-247

destructive detection of flawed hazelnut kernels and lipid oxidation assessment using NIR 248

spectroscopy. J. Food Eng. 2015, 160, 42-48. 249

(6) Huang, H.; Yu, H.; Xu, H.; Ying, Y. Near infrared spectroscopy for on/in-line monitoring 250

of quality in foods and beverages: A review. J. Food Eng. 2008, 87, 303-313. 251

(7) Guthrie, J.; Greensill, C.; Bowden, R.; Walsh, K. Assessment of quality defects in 252

macadamia kernels using NIR spectroscopy. Aust. J. Agric. Res. 2004, 55, 471-476. 253

(8) Rogel-Castillo, C.; Zuskov, D.; Chan, B. L.; Lee, J.; Hong, G.; Mitchell, A. E. Effect of 254

Temperature and Moisture on the Development of Concealed Damage in Raw Almonds (Prunus 255

dulcis). J Agric Food Chem. 2015, 63, 8234-8240. 256

Page 12 of 24



(9) Büning-Pfaue, H. Analysis of water in food by near infrared spectroscopy. Food 257

Chemistry. 2003, 82, 107-115. 258

(10) Jensen, P. N.; Sørensen, G.; Engelsen, S. B.; Bertelsen, G. Evaluation of Quality Changes 259

in Walnut Kernels (Juglans regia L.) by Vis/NIR Spectroscopy. J. Agric. Food Chem. 2001, 49, 260

5790-5796. 261

(11) Liu, J.; Li, X. Y.; Li, P. W.; Wang, W.; Zhang, J.; Zhang, R.; Liu, P. Nondestructive 262

detection of moldy chestnut based on near infrared spectroscopy. Afr. J. Agric. Res. 2010, 5, 263

3213-3218. 264

(12) Moscetti, R.; Haff, R. P.; Saranwong, S.; Monarca, D.; Cecchini, M.; Massantini, R. 265

Nondestructive detection of insect infested chestnuts based on NIR spectroscopy. Postharvest 266

Biol. Technol. 2014, 87, 88-94. 267

(13) Moscetti, R.; Haff, R. P.; Aernouts, B.; Saeys, W.; Monarca, D.; Cecchini, M.; 268

Massantini, R. Feasibility of Vis/NIR spectroscopy for detection of flaws in hazelnut kernels. J. 269

Food Eng. 2013, 118, 1-7. 270

(14) Pannico, A.; Schouten, R. E.; Basile, B.; Romano, R.; Woltering, E. J.; Cirillo, C. Non-271

destructive detection of flawed hazelnut kernels and lipid oxidation assessment using NIR 272

spectroscopy. J. Food Eng. 2015, 160, 42-48. 273

(15) Wang, D.; Dowell, F.; Ram, M.; Schapaugh, W., Classification of fungal-damaged 274

soybean seeds using near-infrared spectroscopy. Int. J. Food Prop. 2004, 7, 75-82. 275

(16) Tillman, B. L.; Gorbet, D. W.; Person, G. Predicting Oleic and Linoleic Acid Content of 276

Single Peanut Seeds using Near-Infrared Reflectance Spectroscopy. Crop Sci. 2006, 46, 2121-277

2126. 278

Page 13 of 24



(17) Bellincontro, A.; Fracas, A.; DiNatale, C.; Esposito, G.; Anelli, G.; Mencarelli, F. Use of 279

NIR Technique to Measure the Acidity and Water Content of Hazelnuts. Acta Hortic. 2005, 686, 280

499-503. 281

(18) Davrieux, F.; Allal, F.; Piombo, G.; Kelly, B.; Okulo, J. B.; Thiam, M.; Diallo, O. B.; 282

Bouvet, J. M. Near infrared spectroscopy for high-throughput characterization of Shea tree 283

(Vitellaria paradoxa) nut fat profiles. J Agric Food Chem. 2010, 58, 7811-7819. 284

(19) Pearson, T. C., Use of near infrared transmittance to automatically detect almonds with 285

concealed damage. LWT--Food Sci. Technol. 1999, 32, 73-78. 286

(20) Nakariyakul, S., Internal damage inspection of almond nuts using optimal near-infrared 287

waveband selection technique. J. Food Eng. 2014, 126, 173-177. 288

(21) Nakariyakul, S.; Casasent, D. P. Classification of internally damaged almond nuts using 289

hyperspectral imagery. J. Food Eng. 2011, 103, 62-67. 290

(22) Metrohm NIRSystem. Herisau, Switzerland. Monograph: NIR Spectroscopy A guide to 291

near-infrared spectroscopic analysis of industrial manufacturing processes. Retrieved April 16, 292

2016 from: http://www.mep.net.au/wpmep/wp-293

content/uploads/2013/05/MEP_Monograph_NIRS_81085026EN.pdf 294

(23) Wehrens, R. Chemometrics With R: Multivariate Data Analysis in the Natural Sciences 295

and Life Sciences. 2011, Springer, Heidelberg. 296

(24) Signal developers. signal: Signal processing. 2015. URL: http://r-forge.r-297

project.org/projects/signal/. 298

(25) Wickham, H. The Split-Apply-Combine Strategy for Data Analysis. J. Stat. Softw. 2011. 299

40, 1-29. URL http://www.jstatsoft.org/v40/i01/. 300

Page 14 of 24



(26) Wickham, H.; Francois, R. dplyr: A Grammar of Data Manipulation. R package version 301

0.4.3. 2015. https://CRAN.R-project.org/package=dplyr 302

(27) Kuhn, M.; Wing, J.; Weston, S.; Williams, A.; Keefer, C.; Engelhardt, A.; Cooper, T.; 303

Mayer, Z.; Kenkel, B.; Benesty, M.; Lescarbeau, R.; Ziem, A.; Scrucca, L.; Tang, Y.; Candan, C. 304

caret: Classification and Regression Training. R package version 6.0-68. 2016. https://CRAN.R-305

project.org/package=caret 306

(28) Govindarajan, K. N.; Kandala, C. V. K.; Subbiah, J. NIR Reflectance Spectroscopy for 307

Nondestructive Moisture Content Determination in Peanut Kernels. Trans. ASABE. 2009, 52, 308

1661-1665. 309

(29) (USDA) US Department of Agriculture, Agricultural Research Service, 2015. USDA 310

National Nutrient Database for Standard Reference, Release 27. Retrieved August 26, 2015 311

from: Nutrient Data Laboratory Home Page: http://ndb.nal.usda.gov/ndb/search 312

(30) Sathe, S.; Seeram, N.; Kshirsagar, H.; Heber, D.; Lapsley, K. Fatty acid composition of 313

California grown almonds. J. Food Sci. 2008, 73, C607-C614. 314

(31) Moscetti, R.; Monarca, D.; Cecchini, M.; Haff, R. P.; Contini, M.; Massantini, R., 315

Detection of Mold-Damaged Chestnuts by Near-Infrared Spectroscopy. Postharvest Biol. 316

Technol. 2014, 93, 83-90. 317

(32) Fassio, A.; Cozzolino, D. Non-destructive prediction of chemical composition in 318

sunflower seeds by near infrared spectroscopy. Ind. Crops Prod. 2004, 20, 321-329. 319

(33) Workman Jr, J.; Weyer, L., Appendix D. Spectra - Structure Correlations for Near 320

Infrared. In Practical guide and spectral atlas for interpretive near-infrared spectroscopy, 321

Second edition; CRC Press: 2012; pp 229 - 267. 322

Page 15 of 24



(34) Jr, A. D. K.; Halbrook, W. U.; Fuller, G.; Whitehand, L. C. Almond Nutmeat Moisture 323

and Water Activity and its Influence on Fungal Flora and Seed Composition. J. Food Sci. 1983, 324

48, 615-617. 325

326

327

328

329

330

331

332

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Abbreviations Used 333

Concealed damage, CD; No concealed damage, NCD; NIR, Near Infrared Spectroscopy; 334

SNV, Standard Normal Variate; PLS, Partial Least Square; DA, Discriminant Analysis; fp, False 335

Positive; fn, False Negative; % ER., percentage error rate. 336

337

Funding Sources 338

The Almond Board of California provided financial support for this study. We would also 339

like to acknowledge the support of the John Kinsella Endowed Chair in Food, Nutrition and 340

Health. 341

342

Acknowledgements 343

The authors thank Franz Niederholzer (UC Farm Advisor Colusa/Sutter/Yuba Counties) 344

for many thoughtful conversations on almond flavor and breeding and for providing almond 345

samples. 346

347

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FIGURE LEGENDS: 348

349

Figure 1. Color development in raw and roasted almonds (120°C for 90 min) exposed to 350

5% moisture (control) and 11% moisture (concealed damage). 351

Figure 2. The mean standard normal variate (SNV) pre-processed absorbance spectra of 352

almonds with no concealed damage (NCD). 353

Figure 3. A comparison of the mean second derivative (Savitzky-Golay, 9-smoothing 354

points) pre-processed spectra of almonds with no concealed damage (NCD) and 355

concealed damage (CD). 356

357

358

359

360

361

362

363

364

365

366

Page 18 of 24



Figure 1 367

368

369

Page 19 of 24



Figure 2 370

371

372

373

374

375

376

377

378

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

1120 1220 1320 1420 1520 1620 1720 1820 1920 2020 2120

Absorbance (log 1/R)

Wavelength (nm)

Page 20 of 24



Figure 3 379

380

381

382

-0.005

-0.004

-0.003

-0.002

-0.001

0

0.001

0.002

0.003

0.004

1120 1220 1320 1420 1520 1620 1720 1820 1920 2020 2120

Second derivative

Wavelength (nm)

NCD

CD

Page 21 of 24



383

Table 1. Results of the PLS-DA model using standard normal variate (SNV), second derivative, 384

and SNV and second derivative pre-processing. 385

a) Calibration Model 386

SNV Second derivative Second derivative + SNV

Wavelength selected

(nm)

1408 – 1465

1902 – 1959

1408 – 1465

1692 – 1740

1902 – 1959

2064 – 2104

1408 – 1465

1692 – 1740

1902 – 1959

# Latent variables 4 7 4

ROC* / % Error rate 0.908/9.2 0.918/8.2 0.918/8.2

Specificity / % fn 0.839/16.1 0.876/12.4 0.840/16.0

Selectivity / % fp 0.874/12.6 0.828/17.2 0.894/10.6

* Area under the Receiver Operating Characteristics 387

b) Validation Model 388

SNV Second derivative Second derivative + SNV

% Error rate 9 7 9

% false positive (% fp) 8 8 9

% false negative (% fn) 11 6 7

389

390

391

392

393

Page 22 of 24



Table 2. Comparison of NIR validation results between methods used in the classification 394

of almond with concealed damage (CD). 395

Pearson, T.C

(1998)

Nakariyakul, S.

(2014)

Nakariyakul et

al. (2011)

Results

Obtained

Herein*

Range (nm) 700 – 1400 700 – 1400

(selected

wavelength)

700 –1400

(selected

wavelength)

1125–2153

(selected

wavelength)

% Error rate 12.4 – 27.5 5.8 8.8 8.2 – 9.2

% False positive 0.7 – 5.4 2.91 – 3.41 0.58 – 1.74 12.4 – 16.1

% False negative 11.1 – 23.8 14.81 – 62.96 31.48 – 53.70 10.6 – 17.2

* Based on calibration models 396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

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Table of Contents Graphics 411

412

413

Page 24 of 24



Documents

identification of concealed damage in raw almonds (Prunus ... · 42 Introduction 43 44 Concealed damage (CD) in raw almonds (Prunus dulcis (Mill.)D.A. Webb) is defined by 45 the industry