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Food Control 50 (2015) 630e636

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Food Control

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Classification of intact açaí (Euterpe oleracea Mart.) and juçara(Euterpe edulis Mart) fruits based on dry matter content by meansof near infrared spectroscopy

Luis Carlos Cunha Junior a, *, Viviani Nardini a, Bed P. Khatiwada b,Gustavo Henrique de Almeida Teixeira c, Kerry B. Walsh b

a Universidade de S~ao Paulo (USP), Faculdade de Ciencias Farmaceuticas de Ribeir~ao Preto (FCFRP), Departamento de An�alises Clínicas, Toxicol�ogicas eBromatol�ogicas, Av. do Caf�e s/n e Campus Universit�ario da USP, Ribeir~ao Preto, S~ao Paulo CEP: 14.040-903, Brazilb Plant Sciences Group, Building 7, Central Queensland University, Rockhampton 4702, Queensland, Australiac Universidade Estadual Paulista, Faculdade de Ciencias Agr�arias e Veterin�arias de Jaboticabal, Vila Industrial, Jaboticabal, Preto, S~ao Paulo CEP: 14.884-900,Brazil

a r t i c l e i n f o

Article history:Received 2 July 2014Received in revised form2 September 2014Accepted 29 September 2014Available online

Keywords:AçaíJuçaraReflectance near infrared spectroscopyPartial Least Squares RegressionPartial least squares-discriminant analysisPrincipal component analysis discriminantanalysis

* Corresponding author.E-mail addresses: cunhajunior.l.c@gmail.com (L.C.

usp.br (V. Nardini), bedkhatiwada@gmail.com (B.Punesp.br (G.H.A. Teixeira), k.walsh@cqu.edu.au (K.B. W

http://dx.doi.org/10.1016/j.foodcont.2014.09.0460956-7135/© 2014 Elsevier Ltd. All rights reserved.

a b s t r a c t

The processing of açaí (Euterpe oleracea Mart.) and juçara (Euterpe edulis Mart) fruit requires wateraddition for adequate pericarp extraction. Currently, the amount of added water is based on fruitmoisture content as estimated using a convection oven method. In this study, diffuse reflectance FTNIRspectra (1000e2500 nm, 64 scans and spectral resolution of 8 cm�1) of intact açaí and juçara fruit wereused to discriminate fruit batches based on the dry matter (DM) content using mature fruit collected overtwo years. Spectra were collected of ~25 fruits per batch, placed on a 90 mm diameter glass dish in asingle layer. The calibration set contained of 371 lots, while the prediction set consisted of 132 lots (ofdifferent locations, times). Spectra were subject to several pre-processing methods and models weredeveloped using Partial Least Squares Regression (PLSR), Partial Least Squares-Discriminant Analysis(PLS-DA) and Principal Component Analysis Discriminant Analysis (PCA-DA). A PLSR model constructedusing the wavelength range of 1382e1682 nm and full multiplicative scatter correction achieved a rootmean square error for prediction on DM of 5.25% w/w with a ratio of the standard deviation of DM set tothe bias corrected RMSEP of 1.5 on the test set. A PCA-DA model based on the same wavelength of regionoutperformed the PLS-DA method to segregate the test population into categories of high (>32 %DM) andlow DM (<32% DM) with 74% accuracy achieved. The PCA-DA technique is recommended to the pro-cessing industry as a non-destructive and rapid method for optimisation of water added during pro-cessing using batch assess of fruit from incoming lots of fruits.

© 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Açaí (Euterpe oleracea Mart.) is endemic to Amazonian flood-plain ecosystems and juçara (Euterpe edulis Mart.) is found in theAtlantic Forest Region (Pessoa & Teixeira, 2012; In�acio, Lima, Lopes,Pessoa,& Teixeira, 2013). Both fruits have been traditionally used asfood in these regions. A typical fruit weights 1e3 g, of which 5e25%is the edible pulp (exocarp andmesocarp - pericarp) that surrounds

Cunha Junior), viviani@fcfrp.. Khatiwada), gustavo@fcav.alsh).

a single seed (Borges et al, 2011; Pessoa & Teixeira, 2012; Schausset al., 2006).

Both fruit have exceptionally high antioxidant activity (Aguiar,Menezes, & Rogez, 2013; In�acio et al., 2013; Poulose et al., 2012),and were included in the top ten “super foods” in 2012 (Pessoa &Teixeira, 2012; Smith, 2013). These fruits have also been used inthe nutraceutical and cosmetic industries due to the reports of ef-ficacy of fruit extracts in, e.g., combating inflammatory and oxida-tive mediators involved in ageing (Poulose et al., 2012; Zhao et al.,2004).

Frozen and dried pulp are produced and exported, with exportsto the United States beginning in 2000, and since expanding toinclude Australia, Europe and Japan. The value of the exportedproduct reached US$ 2.1 million in 2002, and it increased to US$ 17

Table 1Populations of açaí (Euterpe oleracea Mart.) and juçara (Euterpe Edulis Mart.) fruitsand the respective population statistics for dry matter content (DM, %). Each set wascategorized into samples above (0) and below (1) 32% DM.

Species Locality DM Score N

Mean aS.D. Max Min

Açaí (A) Amer (i) 32.5 2.33 37.3 27.0 0 441 59

Jab1 (ii) 40.7 2.75 49.5 37.4 0 01 33

Jab2(iii) 37.0 2.34 44.9 31.7 0 11 139

Juçara (J) Amer (i) 31.2 4.86 42.8 25.7 0 241 15

Jab1(ii) 28.9 6.47 44.1 19.8 0 681 31

Rib(iv) 24.7 3.79 34.6 18.6 0 831 6

A þ J Pop-1 (i þ iii þ iv) 32.2 5.64 44.9 18.6 0 1521 219

Pop-2 (ii) 31.8 7.72 49.5 19.8 0 681 64

a standard deviation.

L.C. Cunha Junior et al. / Food Control 50 (2015) 630e636 631

million in 2012, corresponding to about 6000 tons of pulp, mainlyaçaí (S.E.Agri, 2014).

In Brazil, the quality of fruit pulp is regulated by the BrazilianMinistry of Agriculture and Food Supply (MAPA), which establisheda specification based on total soluble solids content (TSS) of pulp.Three grades are recognized (>14%; 11e14%; and 8e11% TSS; Brazil,2000).

The current production process involves reception, sieving,removal of fruit with visual defects (disease, bruises, insect dam-age), washing, sanitation, blanching, softening of acceptable fruit inwater (35e40 �C, 20 min), processing to separate pulp from seed,homogenization, pasteurization, packaging, freezing and storage(Schwob, 2012). Currently sorting is based on human visual in-spection, but the processing system would suit use of machinevision, as is done for cherry. Fruits are mechanically de-pulped in a1:1 or 1:2 ratio of fruit:water (Rogez, Akwie, Moura, & Larondelle,2012; Silva et al., 2013), followed by homogenization to the cur-rent standards. Product may also be dried by lyophilisation, at-omization (spray-drying), vacuum or other methods (Schwob,2012). The addition of too much water during processing willcause product to fail the specification, and thus involves the cost oflater water removal. On the other hand, the addition of too littlewater results in poor pericarp (pulp) extraction from the fruit.

Thus the amount of water used in the softening process repre-sents a significant management point. Commonly, fruit moisturecontent is measured at reception, prior to fruit sorting, using theconvection oven method (Schwob, 2012). The oven method is timeconsuming (~24 h), and suffers sampling issues, due to wide vari-ation of dry matter (DM) among fruit in a given population. Nearinfrared (NIR) spectroscopy may be an alternative method, condi-tional on the ability to create a robust calibration for this indirectanalysis technique. NIR spectroscopy is used to assess DM of otherintact fruit, for example, Subedi and Walsh (2011) reported the useof interactance spectra (720e920 nm) of intact mango to predictDM content, with root mean square error for prediction (RMSEP)between 0.69 and 1.14 % w/w. Koizimi, Trevelin, Pessoa, CunhaJunior, and Teixeira (2013) reported use of reflectance spectra(1000e2500 nm; acquired using a Spectrum 100N FTNIR, Perki-nElmer company) to assess the DM content of açaí reconstitutedjuice from commercial pulp extracts, reporting a root mean squareerror for cross validation (RMSECV) of 0.95% and ratio of standarddeviation of DM of the calibration set and RMSECV (RPD) of 3.3.

The assessment of fruit DM content normally involves the use ofan interactance optical geometry, with an effective ‘opticallysampled volume’ to a depth of between 10 and 30 mm (Subedi &Walsh, 2011). In contrast, reflectance geometry is considered toresult in optical sampling of intact fruit to a depth of <4 mm(Lammertyn, Peirs, Baerdemaeker, & Nicolaï, 2000). In the currentapplication we desire to minimise information from the seed, andas the pericarp has a thickness between 3 and 8 mm (Pessoa &Teixeira, 2012), a reflectance geometry is appropriate.

NIR spectroscopy estimation of a continuous variable such asDM is typically undertaken using multivariate linear regressiontechniques (e.g. multiple linear regression; principal componentregression; Partial Least Squares Regression), and can be under-taken using non-linear techniques (e.g. artificial neural networks;support vector machines; soft independent modelling of classanalogies; Mukarev&Walsh, 2012). Supervised pattern recognitionanalysis can be used to classify samples based on predeterminedcategories, for example, linear discriminant analysis (LDA) andPartial Least Square-Discriminant Analysis (PLS-DA) (Naes,Isaksson, Fearn, & Davies, 2002). LDA was used by Santos,Nardini, Cunha Junior, Barbosa Junior, & Teixeira (2014) to distin-guish juçara from açaí fruit based on rare earth element concen-tration, achieving an accuracy of external classification of 83.3%.

The evaluation of any proposed technique must include a vali-dation step based on assessment of samples not included in thetraining set. For example, Brito, Brito, Honorato, Pontes, and Pontes(2013), Salguero-Chaparro, Gaitan-Jurado, Ortiz-Somovilla, andPena-Rodriguez (2013) and Sinelli, Cerretani, Egidio, Bendini, &Casiraghi (2010) developed discriminant analyses models basedon only one population of fruit, randomly divided to calibration andvalidation sets. The reported accuracy of 80e95% of is thus opti-mistic, and not a true test of the ability of the model to predict orclassify fruit outside the population used in calibration (Golic &Walsh, 2006). In this study, the use of reflectance FTNIR spectraof intact açaí and juçara fruit to assess fruit dry matter (DM) wasinvestigated content.

2. Materials and methods

2.1. Plant material

Açaí (E. oleracea Mart.) and juçara (E. edulis Mart.) fruitbunches were harvested at commercial maturity (deep purplefruit) several times during the cropping season at four localities(within S~ao Paulo State, Brazil) and in two years (2012 and 2013).A total of fifty bunches were harvested, with each bunch comingfrom a separate tree. The açaí locations were: (i) from a privatelyowned garden in Am�erico Brasiliense (21�420 S latitude, 48�010 Wlongitude, and 646 m altitude), S~ao Paulo State (Amer); (ii)Viveiro Experimental de Plantas Ornamentais and Florestais, partof Universidade Estadual Paulista; Jaboticabal Campus (21�150 Slatitude, 48�150 W longitude and 560 m altitude), Jaboticabal, S~aoPaulo State (Jab 1); and (iii) from a privately owned urbanvegetable garden in Jaboticabal, S~ao Paulo State (Jab 2) (Table 1).Juçara fruit were harvested from localities (i) and (ii), and (iv) inthe garden of Universidade de S~ao Paulo, Faculdade de CienciasFarmaceuticas de Ribeir~ao Preto (21�120 S latitude, 47�4802400 Wlongitude and 546 m altitude), Ribeir~ao Preto, S~ao Paulo State(Rib.) (Table 1).

After harvest, the temperature of fruits were stabilized (~25 �C),and 4-10 lots of 20e30 fruit eachwere randomly selected from eachbunch, creating a total of 503 samples (276 açaí and 227 juçarasamples), of three populations of each of the two species, whichwere combined to create a calibration set (Pop-1) and an inde-pendent test set (Pop-2, Table 1).

L.C. Cunha Junior et al. / Food Control 50 (2015) 630e636632

2.2. Intact fruit spectra acquisition

Fruit samples (20e30 fruit per lot) were placed into a 90 mmdiameter glass dish (PerkinElmer, L118 1257, EUA ref.) in a singlelayer. The plate was placed onto the Near Infrared ReflectanceAccessory of a FT-IR Spectrum 100N (PerkinElmer, Shelton, CT, USA)spectrometer and covered to minimize the incidence of externallight. Diffuse reflectance spectra in the near infrared region (NIR)were obtained over the range of 4000 to 10,000 cm�1 (1000 to2500 nm), with 64 scans per spectra taken at a spectral resolutionof 8 cm�1. The log 1/Reflectance spectra are referred as absorbancespectra, for convenience. Three spectra were acquired per sample,following mixing of fruit between acquisitions of each spectrum.Following NIR spectra acquisition, samples were rapidly frozen andstored at �18 �C.

2.3. Pulp extraction and dry matter content determinationreference method

The exocarp and mesocarp (pericarp - pulp) of each sample(20e30 fruit) were separated from the endocarp (stone) with astainless steel knife, and the resulting material, approximately 9 g,was macerated using a porcelain mortar and pestle following themethod of In�acio et al. (2013). The pulped material was storedat �18 �C to await dry matter (DM) content determination.

The pulp DM content was determined using the AOAC referencemethod (1998, met. 925.23). Two subsamples of pulped material(approx. 2 g) were dried to constant weight in an oven (FANEM®,model 515 SE, S~ao Paulo, Brazil) at 50e60 �C. Fruit DM contentranged from 18.6 to 49.5 %. Fruit were categorized in two sets,below and above average DM (<and >32%, Table 1). This resulted insets of similar size for the low (n¼ 68) and high (n¼ 64) category inthe prediction set.

2.4. Software and data analysis

The chemometric software package The Unscrambler version10.0.1 (Camo, Oslo, Norway) was used. Spectra were pre-processed(full multiplicative scatter correction, MSC, and SavitzkyeGolaywith smoothing window of 15 points, second polynomial order andsecond derivative, d2A). Calibration models were developed basedon Pop-1 and tested on Pop-2 (Table 1).

Partial Least Squares Regression models were developed basedon DM content, with the number of PLS factors based on a leave-one-out cross validation procedure. Model performance wasdescribed by the cross validation correlation coefficient of deter-mination (R2cv), root mean square error of residuals of the crossvalidation (RMSECV), and the ratio of the standard deviation of DMof the calibration set to RMSECV (SDRcv), and in terms of the sta-tistics of prediction of the independent test set, including predic-tion correlation coefficient of determination (Rp2), bias, root meansquare error for prediction on DM (RMSEP), bias corrected RMSEP(SEP) and SDRp. Note that SEP is related to RMSEP and bias by therelationship: SEP¼(

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiRMSEP2 � bias2

p) (Golic & Walsh, 2006).

Principal component analysis (PCA) with all the samples wasfirst used to observe the similarity or segregation of sample groups.PCA-DA and PLS-DA models were based on use of low and high DMcategories. Discriminant model performance was described interms of accuracy:

Accuracy�%�

¼ 100���

Sy1 þ Sy2�

N� 100

�

Where y1 and y2 are the number incorrectly classified sample toeach category, respectively, and N is number of the samples. PCA,

PCA-DA, and PLS-DAmodels were developed using a leave-one-outcross-validation method. PLS-DA is a technique that uses the PLSregular regression methods for performing discriminant analysis,said to be especially useful for high-dimensional data (Naes et al.,2002). PCA-DA involves reduction of data (spectra) dimension-ality using PCA prior to use of the principle component scores in aLinear Discriminant Analysis (LDA) and construction of a projectionspace for all classes (based on Mahalanobis distance). PCA-DA isrecommended to address collinearity in spectral data (Naes et al.,2002). The first 10 principal components of the PCA were used inthe LDA analysis.

3. Results and discussion

3.1. Spectra

DM content of açaí and juçara fruit batches varied between 18.6and 49.5 % (Table 1). On average, fruit with low DM showed higherapparent absorbance (log 1/R) values than fruit with high DM (alsosee difference spectra, Fig. 1A). This phenomenon is consistent withincreased scattering of light by the lower DM fruit, inferring smallercell size and more watereair interfaces. The absorbance spectraalso displayed major features associated with the OeH first over-tone region (1382e1682 nm) and OeH combinations(1900e2150 nm) related to high water content (moisture) of intactfruits. Thus spectra may hold information on fruit DM on either onindirect basis (scattering) or direct basis (OeH absorption features).

The pre-processing routines of multiplicative scatter correction(Fig. 1B) and SavitzkyeGolay second derivatisation (Fig. 1C) areoften employed with reflectance spectra of biological materials toreduce spectral baseline shift issues. For example, In�acio et al.(2013) employed these spectral treatments in a study of anthocy-anin content determination in intact açaí and juçara fruits by NIRspectroscopy. MSC treatment reduced the overall spectral offset ofthe two categories of fruit and focussed attention on the waterfeature at 1900 nm. The second derivative treatment had a similaroutcome.

If DM is consistently associated with scattering, then inclusionof this spectral informationmay improve anymodel describing DM.However, the negative correlation between raw absorbance spectraand DM content at any given wavelength was relatively weak(Fig. 1D), but highest in the range 1350e1870 nm. As such, thisrange is recommended for construction of models based on rawabsorbance spectra. A stronger correlation for individual wave-lengths existed for MSC corrected spectra, e.g. over the range1900e2017 and for second derivative spectra (R > 0.7) (Fig. 1D). Inthe second derivative data, information was present in manywavelength regions.

PCA analysis was undertaken to gauge the level of spectralvariance between populationS based on DM category (n ¼ 503).There was strong overlap of categories, with a little separationalong the PC-1 axis, for a PC plot based on 1382e1682 nm absor-bance data (Fig. 2A), or after MSC (Fig. 2B) or second derivative pre-treatment (Fig. 2C). Samples with high residual variance (within thegray circle the top of Fig. 2B and Fig C) and leverage (within the graycircle the bottom of Fig. 2B) belonging to low-DM category areidentified. High leverage indicates that these samples have a largeinfluence on the model and samples with high residual are not welldescribed in the models (Naes et al., 2002).

3.2. PLSR models with different wavelength and pre-processing

The calibration (Pop-1) and prediction (Pop-2) sets were dividedby locality with three localities included in the calibration set(Amer, Jab 2 and Rib) and a different locality (Jab1) in the prediction

Fig. 1. Average and difference spectra from high and low dry matter categories for (A) log 1/R or absorbance, (B) multiplicative scatter correction (MSC) treated log 1/R, (C) secondderivative of log 1/R (d2A), and (D) correlation between dry matter contents (DM, %) and log 1/R, MSC and d2A at each wavelength for combined populations of açaí (Euterpeoleracea Mart) and juçara (Euterpe edulis Mart) fruits. Difference spectra ¼ (low DM e high DM absorbance) � 10.

Fig. 2. Score plots for PC 1, 2 and 3 from a principal component analysis over range of 1382e1682 nm with (A) absorbance, (B) multiplicative scatter corrected and (C) secondderivative for combined populations of açaí (Euterpe oleracea Mart.) and juçara (Euterpe edulis Mart.) fruits.

L.C. Cunha Junior et al. / Food Control 50 (2015) 630e636 633

set (Table 1). Thus the models are tested in predicted using a trulyindependent population, grown under different conditions.

PLSR models using a number of wavelength regions were tested(data not shown). The full spectra range (1000e2500 nm) pre-sented the best calibration result, although at expense of morefactors (e.g. PC ¼ 8 for log 1/R spectra) and a relatively poor

performance in prediction of the independent set (i.e. model wasover-fitted). A model based on the region 1382e1682 nmwas morerobust, with Rp

2 of 0.60 for log 1/R spectra. The use of MSC or d2A didnot improve the calibration statistics of models built on thiswavelength range (Table 2), however, the PLSR model based onMSC treated spectra of the region 1376e1882 nm gave the best

Table 2Performance in calibration and prediction of PLS models statistics for example wavelength regions and pre-processing techniques, based on NIR reflectance spectra for drymatter content (DM, %) of açaí (Euterpe oleracea Mart.) and juçara (Euterpe edulis Mart.) fruits from the populations described in.Table 1.

Wavelength (nm) Calibration (Pop-1, n ¼ 371, SD ¼ 5.64) Prediction (Pop-2, n ¼ 132, SD ¼ 7.72)

Pre-process PC Rcv2 RMSECV SDRcv Rp

2 RMSEP Bias SDRp

1000e2500 Nil 8 0.79 2.56 2.2 0.48 5.64 0.04 1.41376e1882 Nil 3 0.74 2.86 2.0 0.54 5.38 �0.42 1.41382e1682 Nil 5 0.76 2.74 2.1 0.60 5.20 �0.36 1.51000e2500 MSC 9 0.81 2.45 2.3 0.54 5.43 0.17 1.41376e1882 MSC 5 0.77 2.73 2.1 0.63 5.03 �0.38 1.51382e1682 MSC 5 0.76 2.75 2.1 0.57 5.25 �0.17 1.51000e2500 d2A 7 0.80 2.56 2.2 0.46 5.99 0.48 1.31376e1882 d2A 4 0.76 2.78 2.0 0.45 5.92 0.23 1.31382e1682 d2A 3 0.75 2.83 2.0 0.46 5.81 0.24 1.3

L.C. Cunha Junior et al. / Food Control 50 (2015) 630e636634

prediction result, with Rp2 of 0.63 (Table 2). The regression coeffi-

cient plot of these models (Fig. 3) included weighting around 1,400,1900 and 2200 nm, which is interpreted as associated with OeHfirst overtone region and first overtone of CeH combination(Nicolaï et al., 2007)

A RMSEP of around 5% w/w DM and SDRp of 1.5 were achieved(Table 2). These results are inferior to that observed in other studieswith NIR spectroscopy assessing DM content in intact fruits. Thisresult may be associated with pericarp thickness. For example,Subedi and Walsh (2011) reported that DM assessment in mangofruit was best attempted after the fruit have reached a certain size,such that the optically sampled volume does not reach the seed.Variation in pericarp thickness is likely to be an issue in the currentstudy, potentially impacting DM model performance.

3.3. Discriminant analysis

A PLS-DA model achieved an accuracy of 83.8% in calibration,and 67% in prediction of an independent set (using1376e1882 þ 1896e2012 nm absorbance spectra) (Table 3).

The wavelength region employed impacted the predictionaccuracy, however the model developed using MSC or second

Fig. 3. Regression coefficients for PLSR using spectra wavelength of 1000e2500 nm for log 1R (d2A) for populations of açaí (Euterpe oleracea Mart) and juçara (Euterpe edulis Mart) fru

derivative of SavitzkyeGolay were not better than those withoutspectral pre-processing (Table 3). The PCA-DA models obtainedusing the full wavelength range of absorbance spectra(1000e2500 nm) showed the best calibration accuracy(85.4e93.0 %, Table 4), however the highest predictive ability(72%) was achieved using the wavelength region of1382e1682 nmwithout pre-processing (Table 4). The informationremoved by MSC and derivatisation is evidently useful todiscrimination of fruit DM level.

4. Conclusions

Despite the thin and variable pericarp thickness, NIR reflectancespectroscopy was demonstrated to be a useful technique to sortintact açaí and juçara fruit based on dry matter content. PLSRmodels achieved a RMSEP of 5.03, while the supervised patternrecognition technique of PCA-DA achieved 72% accuracy indiscriminating fruit into two categories (high, above 32%, and low,below 32% w/w DM content). While not allowing precise control ofwater addition, at-line NIR spectroscopy of incoming fruits batcheswould nonetheless be useful for coarse control of water additionduring processing.

/R or absorbance, multiplicative scatter correction (MSC) and second derivative of log 1/its.

Table 3PLS-DA classification and prediction of dry matter (DM) content categories of açaí (Euterpe oleraceaMart.) and juçara (Euterpe edulisMart.) fruits from the population describedin Table 1. Accuracy rate was calculated using a leave-one-out cross-validation, with 0.5 as the discriminant level.

Wavelength (nm) Pre-process PC Calibration (Pop-1, n ¼ 371) External classification (Pop-2, n ¼ 132)

Incorrectly classified sample Accuracy rate (%) Incorrectly classified sample Accuracy rate (%)

Low (n ¼ 152) High (n ¼ 219) Low (n ¼ 68) High (n ¼ 64)

1000e2500 Nil 9 33 33 82.2 2721

63.6

1382e1682 Nil 6 41 41 77.9 3220

60.6

1376e1882 þ 1896e2012 Nil 11 33 32 82.5 2224

67.4

1000e2500 MSC 11 30 30 83.8 3018

63.6

1382e1682 MSC 8 37 37 80.1 2027

64.4

1376e1882 þ 1896e2012 MSC 11 33 33 82.2 2322

65.9

1000e2500 d2A 7 32 31 83.0 3321

59.1

1382e1682 d2A 4 39 38 79.2 2421

65.9

1376e1882 þ 1896e2012 d2A 7 33 33 82.2 3519

59.1

Table 4PCA-DA classification and prediction from different dry matter (DM) categories of açaí (Euterpe oleracea Mart.) and juçara (Euterpe edulis Mart.) fruits from the populationsdescribed in.Table 1.

Wavelength (nm) Pre-process Calibration (Pop-1, n ¼ 371) External classification (Pop-2, n ¼ 132)

Incorrectly classified sample Accuracy rate (%) Incorrectly classified sample Accuracy rate (%)

Low (n ¼ 152) High (n ¼ 219) Low (n ¼ 68) High (n ¼ 64)

1000e2500 Nil 7 19 93.0 5 46 61.41382e1682 Nil 4 50 85.4 0 37 72.01376e1882 þ 1896e2012 Nil 7 28 90.6 0 47 64.41000e2500 MSC 7 22 92.2 9 42 61.41382e1682 MSC 5 31 90.3 5 46 61.11376e1882 þ 1896e2012 MSC 10 21 91.6 66 1 49.21000e2500 d2A 3 34 90.6 0 64 51.51382e1682 d2A 3 74 79.2 1 63 51.51376e1882 þ 1896e2012 d2A 5 43 87.1 0 64 51.5

L.C. Cunha Junior et al. / Food Control 50 (2015) 630e636 635

Acknowledgements

The authors would like to thank the Fundaç~ao de Amparo �aPesquisa do Estado de S~ao Paulo (FAPESP) for the financial supportof this research (Proc. 2008/51408-1, 2011/19669-2) and forproviding the BEPE scholarship (Proc. 2013/0.6089-3).

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