Estimating sensory properties of common beans (Phaseolus vulgaris L.) by near infrared spectroscopy

Food Research International 56 (2014) 55–62

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Estimating sensory properties of common beans (Phaseolus vulgaris L.) by near infrared spectroscopy Marçal Plans a,⁎, Joan Simó a, Francesc Casañas a, Roser Romero del Castillo a, Luis E. Rodriguez-Saona b, José Sabaté a a b

Departament d'Enginyeria Agroalimentària i Biotecnologia, Universitat Politècnica de Catalunya, Esteve Terradas 8, Castelldefels 08860, Spain Department of Food Science & Technology, The Ohio State University, 110 Parker Food Science and Technology Building, 2015 Fyffe Road, Columbus, OH 43210, United States

a r t i c l e

i n f o

Article history: Received 6 October 2013 Accepted 7 December 2013 Keywords: Common beans NIRS Gene-bank Sensory analysis Partial least square regression

a b s t r a c t Near infrared spectroscopy (NIRS) has been widely used to determine food chemical composition and to a lesser extent to evaluate sensory properties. Because sample preparation is relatively simple, NIRS is especially useful in situations where many samples must be analysed, such as gene-bank characterization or breeding. We aimed to assess the feasibility of using NIRS to predict aroma, flavour, mealiness, seed-coat perception, seed-coat brightness, and seed-coat roughness in common beans. Spectra of raw, undried cooked and dried cooked common bean seeds of 55 accessions were registered. Partial least squares (PLS) regression equations were developed between spectra absorbance and sensory properties scored by eleven trained panellists. Spectra registered on dried cooked samples generally yielded the best predictions. The relative ability of prediction (RAP) values were greater than 0.8 for flavour and mealiness and between 0.5 and 0.7 for seed-coat roughness and brightness. However, a suitable model to estimate the seed-coat perception was not found. These results make it possible to screen for samples that are close to the target sensory properties and thus substantially reduce the number of panel sessions needed for gene-bank evaluation or breeding. © 2013 Elsevier Ltd. All rights reserved.

1. Introduction In recent years, health concerns have increased legume consumption in developed countries and thus the demand for varieties with high sensory value. Some landraces of beans have special sensory characteristics that are highly valued by consumers. Improving the agronomic aspects of these varies while maintaining or enhancing their organoleptic properties should lead to high quality materials that can be profitable for farmers (Almirall, Bosch, Romero del Castillo, Rivera, & Casañas, 2010; Bosch, Casañas, Sánchez, Pujolà, & Nuez, 1998). However, the evaluation of organoleptic attributes requires a trained panel of judges using standardized working methods. This approach involves considerable work and time. The logistics are complicated: samples must be rigorously prepared and several judges must meet several times because only a few samples can be evaluated in each session. On the other hand, thousands of entries are preserved in gene-banks but few are known about their sensory characteristics preventing them of being used in breeding programmes. A survey made on 35 gene bank members of the GRIN: (Germplasm Resources Information Network, United States Department of Agriculture) and ECP/GR (European Cooperative Programme for Plant Genetic Resources) nets concluded that less than 5% of the accessions were characterized for quality aspects

⁎ Corresponding author. Tel.: +1 614 743 3180. E-mail address: [email protected] (M. Plans). 0963-9969/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodres.2013.12.003

and only few representatives of this percentage were characterized for sensory quality. Near infrared spectroscopy (NIRS) has widely been used for food evaluation because it is fast and requires only minimum sample treatment. NIR infrared spectral information for analytical purposes relies on the multivariate approach for calibration (chemometrics). PLS, first introduced by Wold (1975) under the name NIPALS (nonlinear iterative partial least squares) focuses on maximizing the variance of the dependent variables (i.e. NIR spectra) explained by the independent ones (i.e. sensory attributes). The main advantage of these techniques is to avoid co-linearity problems permitting to work with a number of variables that is greater than the number of samples. To develop a robust model the samples must include the natural variability of the concentration or property of interest. Thus, the number of samples employed for calibration has been considered critical, with recommendations for multicomponent samples in the range of 50 to 100 samples, depending on the complexity and variability of the matrix accompanying actual samples. Because of PLS's nature as a secondary method it requires that a conventional, well-accepted supporting (reference) method be available to supply the analytical results required for the modelling step of NIR spectral data. Furthermore, models should be frequently updated to accommodate changes in the sample matrix (Pasquini, 2003). Extensive reviews about its use for meat, dairy products, and vegetables have been published (Butz, Hofmann, & Tauscher, 2005; Karoui & De Baerdemaeker, 2007; Prieto, Roehe, Lavín, Batten, & Andrés, 2009).

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Although most NIRS studies deal with determining chemical composition or physical properties, a growing number of papers report the use of NIRS to evaluate sensory attributes. However, NIRS is often much less accurate for predicting sensory properties than for determining chemical composition, because sensory properties are influenced by the food structure, the composition of the material, the complex interactions among its chemical components (some of which are present in low concentrations), and the interactions with the perception ability of each judge (Natsuga, Egashira, Sue, Ikeda, & Ooba, 2008; Prieto et al., 2009). Several correlations have been established between NIR spectra and sensory properties of foods derived from plants. In wine, NIRS reflects overall flavour, sweetness, and aroma descriptors such as honey, passion fruit, and lemon-citrus (Cozzolino et al., 2005; Cozzolino et al., 2006). In coffee beverages, acidity, bitterness, mouth feel, flavour, cleanliness, body, aftertaste, and overall quality have been estimated (Esteban-Díez, González-Sáiz, & Pizarro, 2004; Ribeiro, Ferreira, & Salva, 2011). Moreover, NIRS predicted crunchiness, sweetness, and bitterness for chicory hybrids and twelve mouth-feel and four appearance descriptors of cooked potatoes (Boeriu, Yuksel, de Vries, Stolle-Smits, & van Dijk, 1998; François et al., 2008; van Dijk et al., 2002). Attempts to apply NIRS to intact samples of fruits and vegetables have often been limited by sample heterogeneity, so samples are usually milled or blended (Butz et al., 2005). To our knowledge, the few studies reported about legumes all deal with peanuts and frozen or fresh peas. The attempts to obtain good NIRS calibrations for peanuts taste test parameters failed (Natsuga et al., 2008). In frozen peas, one study found that NIR spectra correlated with hardness, mealiness, pea flavour, sweetness, fruity flavour, and off flavour, whereas another found correlations with colour, appearance, sweet flavour, fruity flavour, earthy flavour, off flavour, hardness, skin strength, mealiness, juiciness, and aftertaste (Kjølstad, Isaksson, & Rosenfeld, 1990; Martens & Martens, 1986). In fresh peas, NIR spectra predicted the firmness of skin, firmness of flesh, sweet flavour, strength of pea flavours, and brightness of colour (Chalucova, Krivoshiev, Mukarev, Kalinov, & Scotter, 2000). We aimed to assess the feasibility of NIRS on raw and cooked common beans for predicting the aroma, flavour, mealiness, seed-coat perception, seed-coat brightness, and seed-coat roughness to explore its potential contribution to scan large collections of samples in order to identify potential candidates to be included in breeding programmes.

evaporation (but simmering was maintained at all times). The beans were kept covered with water at all times. When the beans were cooked, determined by the morphology, colour, and texture of each variety, 2.5 g NaCl was added and maintained at 80 °C for up to 2 h until the sensory test was performed. A 180 g portion of these cooked beans was removed, drained for 5 min, and reserved for NIRS analysis. Samples were submitted to a panel consisting of 11 members who were previously trained over a 2-year period (Romero del Castillo, Valero, Casañas, & Costell, 2008). Each sample was made up of 30 g of beans served on a small plate at a temperature of 70–80 °C and was identified by three-digit codes chosen at random. Five different samples were evaluated in each tasting session, and the 55 accessions were tested in duplicate in different independent sessions. The full experiment consisted of twenty-two tasting sessions, distributed twice a week during 3 months. All tasting sessions took place in individual booths meeting the standards set forth by the International Organization for Standardization (ISO 8589, 2007). The intensity of each attribute was quantified on a 10 cm semi-structured scale with the extremes labelled with corresponding descriptions, as shown by Romero del Castillo et al. (2008) and Romero del Castillo et al. (2012). Briefly, for seed coat brightness, the score of 0 represented a dull seed like Tolosa and 10 a very bright seed like Ganxet Montcau. For seed-coat roughness, 0 represented a smooth seed coat like the reference Tolosa bean, and 10 represented a very rough seed coat like the reference Ganxet Montcau bean. For seed-coat perceptibility, 0 stood for extremely low perceptibility, like the Ganxet Montcau bean boiled with distilled water, and 10 stood for very high perceptibility, like the Ganxet Montcau bean boiled in water containing 200 ppm of Ca. For mealiness, 0 meant high creaminess like the Ganxet Montcau bean boiled in distilled water and 10 meant high mealiness like the Tolosa bean cooked in distilled water. For flavour, 0 represented no bean flavour and 10 stood for very intense bean flavour like Tolosa. Finally, for aroma, 0 represented no bean aroma and 10 stood for a high bean aroma like Tolosa. Montcau is a medium-sized, white, flat, and very hooked bean of the Ganxet market class (Santalla, de Ron, & Voysest, 2001), while Tolosa is a mediumsized, black, and round bean of the Negro Brillante market class (Santalla et al., 2001).

2. Materials and methods

Three types of samples were prepared for NIRS analysis: (1) raw beans without cooking that were dried at 55 °C until constant weight and then ground, (2) drained cooked beans that were mashed containing a high proportion of water (average of 2.1 g water per g dried bean) and (3) drained cooked beans that were dried at 55 °C until constant weight and then ground. A Perten 3100 Laboratory mill (Perten Instruments Inc., Springfield IL, USA) with a 0.4 mm screen was used for grinding. The three types of samples were stored in polyethylene bags at − 18 °C in a nitrogen-modified atmosphere until analysis. Before NIRS analysis, samples were defrosted and ground samples were dried again at 55 °C for 4 h. Spectra were recorded from the three types of samples (raw beans, undried cooked beans, and dried cooked beans) and registered using a model 5000 spectrophotometer (Foss NIRSystems, Silver Spring, MD, USA) equipped with a rapid content analyzer (RCA) module. About 5 g of ground beans were placed in a 3 cm diameter cell holder and gently compressed with a cylindrical piece of metal. About 3 g of undried cooked samples were placed in a 5 cm diameter quartz cuvette for trans-reflectance. Spectra were recorded every 2 nm between 1100 nm to 2500 nm and averaged from 32 scans. Three spectra were registered for each sample and the average spectrum was used for computations. The reflectance at each wavelength was expressed as log(1/R). Vision software, version 2.51, (Foss NIRSystems, Silver Spring, MD, USA) was used to control the recorder, collect the spectra, and import the data.

We studied 55 accessions of Spanish landraces and inbred lines selected to encompass a wide range of variation in sensory properties, comprising 42 varieties from the “Spanish core collection” and 13 recombinant inbred lines of Xana × Cornell (Pérez-Vega, Campa, De la Rosa, Giraldez, & Ferreira, 2009; Pérez-Vega et al., 2010). The numbers of samples were similar to those reported for applications of NIR to predict sensory properties (Kjølstad et al., 1990; Ribeiro et al., 2011; Srisawas, Jindal, & Thanapase, 2007). In sensory trials, the individual error associated to each sample increases parallel to the total number of them, so, the number of samples that can be managed in these studies is limited (Meilgaard, Civille, & Carr, 2007; Muñoz, Civille, & Carr, 1992). 2.1. Sensory analysis Seeds were cooked using the protocol described by Romero del Castillo, Costell, Plans, Simó, and Casañas (2012). A sample of 250 g of beans was soaked in 750 mL distilled water for 12–14 h, drained, placed in a thick-bottomed two-litre stainless steel pot, and covered with cold distilled water (1 cm above the level reached by the beans). The pot was brought to a boil, the heat was then lowered to the minimum and the beans were cooked with a lid on (but steam was allowed to escape). During the cooking process, the level of the water was controlled and cold distilled water was added 2 or 3 times to compensate for

2.2. Spectra measurement

M. Plans et al. / Food Research International 56 (2014) 55–62

2.3. Statistical analyses

Table 1 Results of the sensory analysis. 41 calibration samples.

Sensory data provided by the panel were analysed using ANOVA to ensure that significant variation had been detected for all the recorded traits. Main factor genotype (α1), panellist (βj), and their interaction (γij) were included in the model of Eq. (1): γ ij ¼ μ þ α 1 þ β j þ γij þ εij

57

ð1Þ

where γij was each individual scored value for a trait, μ was the grand mean, and εij was the error of the model following a N ~ (0,σ 2). Least significant difference (LSD) with α = 0.05 was used to compare the accessions. The average of each sensory trait for each accession was used as reference value to correlate with NIR spectra. To remove the multiplicative interference of scatter and particle size, each spectrum was pretreated by standard normal variate (SNV) (Barnes, Dhanoa, & Lister, 1989). Then, its first and second derivatives were calculated by the Savitzky and Golay (1964) method to reduce peak overlap and eliminate baseline shift. Principal components analysis (PCA) was used to analyse the spectra outlier (Souza et al., 2011; Wold, Esbensen, & Geladi, 1987). Partial least squares (PLS) regression was used to obtain the equations to correlate NIR spectra (55 samples by 700 wavelength) with sensory properties (Cadena, Cruz, Faria, & Bolini, 2012; Gómez-Caravaca, Maggio, Verardo, Cichelli, & Cerretani, 2013; Martens & Naes, 1989). For any sensory attribute, accessions were divided into 2 groups so that about 3/4 could be used for calibration and 1/4 for external validation (Brown, Bricklemyer, & Miller, 2005). Calibration and validation accessions were randomly selected but they were adjusted so that their content standard deviations were similar to ensure that the range and distribution of the two groups would be comparable. PLS regressions for calibration were evaluated using cross-validation (CV) leaving out about 1/5 of the calibration samples. Coefficient of determination (R2), standard error (SE) and root mean standard error (RMSE) were calculated for both cross-validation (RMSECV) and external validation or prediction (RMSEP). For all the parameters analysed, the mathematical pre-treatment that yielded the minimum standard error of crossvalidation (SECV) value was considered the optimal. The model's predictive ability was assessed with the dimensionless parameters RPD and RAP defined in Eqs. (2) and (3): RPD ¼

SD SEP

ð2Þ

RAP ¼

SD2 RMSEP2 SD2 −S2ref

ð3Þ

where SD is the standard deviation of sensory data in validation samples and Sref is a standard error that indicates the uncertainty of the analysis due to the judges for validation samples as well (Martens & Martens, 1986). The parameter RAP takes into account both the error of NIRS prediction and the uncertainty of panellist. A commercial software (Unscrambler v. 9.2, Camo AS, Thondheim, Norway) and R statistical system (available free of charge through http://www.r-project.org) were used for all computations. 3. Results and discussion 3.1. Sensory analysis ANOVA showed statistical significance (p ≤ 0.05) for both genotype and panellist factors. Differences among groups of accession's mean values were detected, so ensuring a good range of data to be correlated with the NIR registers is critical for developing robust calibration models. Tables 1 and 2 show the means, ranges and standard deviations for sensory properties scored by the panel. The sensory scores for

Aroma Flavour Mealiness Seed-coat perception Seed-coat roughness Seed-coat brightness

Min.

Max.

Mean

SD

Sref

2.34 2.19 2.13 1.01 1.71 2.54

5.65 6.40 7.87 6.11 7.84 8.30

3.98 4.04 5.00 3.04 4.61 5.62

0.94 1.06 1.58 1.35 1.84 1.62

0.60 0.56 0.52 0.62 0.59 0.64

Aroma (0 means no bean aroma and 10 represents Tolosa reference aroma intensity). Flavour (0 means no bean flavour and 10 represents Tolosa reference flavour intensity). Mealiness (0 represents Ganxet Montcau reference intensity and 10 represents Tolosa reference). Seed-coat perception (0 represents Ganxet Montcau (cooked with distilled water) reference intensity and 10 represents Ganxet Montcau (cooked with 200 ppm of Ca) reference intensity). Seed-coat roughness (0 represents Tolosa reference intensity and 10 represents Ganxet Montcau reference intensity). Seed-coat brightness (0 represents Tolosa reference intensity and 10 represents Ganxet Montcau reference intensity).

mealiness, seed-coat perception, seed-coat brightness, and seed-coat roughness covered a wide range of values representing the diversity of the common bean selections used in this study. For aroma and flavour, judges seemed reluctant to assign extreme values in the upper end of the scale, which leads to a narrower range. Seed-coat roughness and brightness were strongly correlated (r = 0.86) (Table 3), because wrinkled seed coats of cooked beans tended to retain more water than smooth ones and thus had a brighter appearance. The sensory properties of mealiness, flavour, and aroma depended on the properties of the whole seed, whereas seed coat brightness, roughness and perception were associated only with the seed coat, which comprises 8% to 15% of the mass of the whole seed. Interestingly, panellists' evaluations showed correlations of seed-coat roughness and brightness with mealiness and to a lesser degree with flavour and aroma. By contrast, seed-coat perception did not correlate with any other property. Bean seed coats are composed of sclereid cells that have undergone a secondary process of lignification (McDougall, Morrison, Stewart, Weyers, & Hillman, 1993). Pectin is found in the middle lamellae and its degree of solubilization determines the degree to which cells separate from one another. As discussed elsewhere in Plans, Simó, Casañas, and Sabaté (2012), seed-coat perception depends on the cross-linking of pectins through Ca and Mg divalent ions and thus it is influenced not only by the content of components but also by their relative ratios and the interactions among them as well. Large systematic studies are needed to elucidate the limited correlations found between seed coat perception and other sensory attributes.

Table 2 Results of the sensory analysis. 14 validation samples.

Aroma Flavour Mealiness Seed-coat perception Seed-coat roughness Seed-coat brightness

Min.

Max.

Mean

SD

Sref

2.35 2.42 2.38 2.07 2.53 3.91

5.20 5.48 7.10 6.24 7.24 8.06

3.69 3.98 4.95 3.76 4.95 6.25

0.80 1.16 1.59 1.48 1.63 1.43

0.73 0.55 0.52 0.67 0.68 0.72

Aroma (0 means no bean aroma and 10 represents Tolosa reference aroma intensity). Flavour (0 means no bean flavour and 10 represents Tolosa reference flavour intensity). Mealiness (0 represents Ganxet Montcau reference intensity and 10 represents Tolosa reference). Seed-coat perception (0 represents Ganxet Montcau (cooked with distilled water) reference intensity and 10 represents Ganxet Montcau (cooked with 200 ppm of Ca) reference intensity). Seed-coat roughness (0 represents Tolosa reference intensity and 10 represents Ganxet Montcau reference intensity). Seed-coat brightness (0 represents Tolosa reference intensity and 10 represents Ganxet Montcau reference intensity).

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M. Plans et al. / Food Research International 56 (2014) 55–62

Aroma Flavour Mealiness Seed-coat perception Seed-coat roughness

Flavour

Mealiness

Seed-coat perception

Seed-coat roughness

Seed-coat brightness

0.62⁎⁎⁎ 1 – – –

0.53⁎⁎⁎ 0.68⁎⁎⁎ 1 – –

0.08 0.21 0.23 1 –

−0.33⁎ −0.4⁎⁎ −0.69⁎⁎⁎ −0.24 1

−0.4⁎⁎ −0.46⁎⁎⁎ −0.66⁎⁎⁎ −0.18 0.86⁎⁎⁎

Differences significant at: 0.05–0.01⁎, 0.01–0.001⁎⁎, and b0.001⁎⁎⁎.

3.2. NIR spectra versus sensory properties

D2(SNV(log (1/R)))

Table 3 Pearson correlation between mean sensory variables using all samples.

1100

SNV(log (1/R))

Fig. 1 shows the SNV spectrum of the three types of samples; the overlapping of different chemical-bond vibrations results in broad bands and poorly defined peaks. The spectrum of the undried cooked samples differs from those of the other two sample types in the regions around 1400 and 1940 nm due to water absorption. PCA of the SNV-pretreated spectra found no H-outliers (Mahalanobis distance, H N 3). The first two principal components explained 94%, 97%, and 92% of the variation for the raw, undried cooked, and dried cooked samples, respectively, and no clusters were observed in the score plots. To obtain the calibration equations, we defined the optimal number of PLS terms as the number of factors that did not significantly reduce the SECV when they were increased. Nevertheless, to prevent overfitting, an upper limit of optimal PLS terms was set at one PLS factor per ten samples of calibration, plus 2 (Ruiz-Jiménez, Priego-Capote, & Luque de Castro, 2006). SNV + 2D treatment led to spectra with more defined peaks (Fig. 2) and yielded the best predictions for all sample types and sensory properties. Tables 4, 5 and 6 summarize the results. The parameters R2 and RPD showed that NIRS was the worst at predicting seed-coat perception: fitting predicted vs. reference values leads to a nearly horizontal straight line for all three sample types (R2CV 0.1–0.4). NIRS was the best at predicting mealiness in both dried cooked (R2pred 0.8 and RPD of 1.9) and undried cooked (R2pred 0.6 and RPD of 1.5) samples. The dimensionless parameter RPD is the most commonly used statistic to evaluate the predictive ability of NIRS, and a threshold value of about 2 is usually required to perform a rough screening (Williams, 2001). The performance of our best models showed RPD values of 1.9 highlighting the lower accuracy of NIRS at predicting sensory properties than those reported for chemical or physical properties. For instance, an extensive review of the application of NIRS to determine parameters of meat quality reported that most RPD values for sensory attributes were around 1, whereas their values for chemical composition were much higher, in part because instrumental measurements are usually more precise than those of expert judges (Prieto et al., 2009).

1300

1500

1700

1900

2100

2300

2500

Wavelength (nm) Fig. 2. Average of second derivative (2D) of NIR spectra. ··· raw, _____ undried cooked, and - - dried cooked.

Thus, the parameter RAP relating the capability of NIR prediction to the precision of the panellists' evaluation has been proposed (Chalucova et al., 2000; Kjølstad et al., 1990; Martens & Martens, 1986). The best value for RAP is 1, which implies that the RMSECV is equal to the standard error of the uncertainty of the panel evaluation. By contrast, RAP = 0 means that RMSECV is equal to the standard deviation of the sensory data in all the samples and indicates that NIRS cannot predict the sensory properties. The highest values of RAP for flavour (0.81), mealiness (0.83) and seed-coat brightness (0.69) were achieved with dried cooked common beans. For the other properties, RAP values were below 0.55 regardless of sample processing type except for flavour (0.61) and seed coat brightness (0.74) for raw beans and mealiness (0.62) for undried cooked beans. NIR predictions for cooked dried samples vs. reference values of sensory properties for calibration and validation samples sets are represented in Fig. 3 (A to F). The relative ability of prediction (RAP) values for flavour, mealiness and seed-coat brightness were comparable to those reported for sensory properties in peas (0.50 b RAP b 0.82) and rice (0.3 b RAP b 0.6) (Kjølstad et al., 1990; Martens & Martens, 1986; Windham et al., 1997). During cooking, foods undergo both chemical and structural changes that modify their interaction with electromagnetic radiation. Thus, registering NIR spectra from cooked bean samples should increase the capability of prediction of their sensory properties instead using spectra

Table 4 Statistical descriptors for the NIRS determinations in raw beans. Sensory trait

PLSfactors

R2cv

SECV

R2pred

RMSEP

SEP

RPD

RAP

Aroma Flavour Mealiness Seed-coat perception Seed-coat roughness Seed-coat brightness

3 4 5 1 5 3

0.20 0.39 0.44 0.12 0.33 0.44

0.98 0.84 1.25 1.28 1.56 0.89

0.34 0.55 0.43 0.03 0.40 0.59

0.78 0.84 1.21 1.75 1.20 0.95

0.75 0.78 1.24 1.70 1.24 0.89

1.07 1.48 1.28 0.87 1.32 1.61

0.29 0.61 0.47 −0.50 0.55 0.74

Table 5 Statistical descriptors for the NIRS determinations in undried cooked beans.

1100

1300

1500

1700

1900

2100

2300

2500

Wavelength (nm) Fig. 1. Average of NIR spectra. ··· raw, _____ undried cooked, and - - dried cooked.

Property

PLSfactors

R2cv

SECV

R2pred

RMSEP

SEP

RPD

RAP

Aroma Flavour Mealiness Seed-coat perception Seed-coat roughness Seed-coat brightness

3 6 3 1 3 3

0.13 0.03 0.73 0.11 0.69 0.66

0.89 1.05 0.83 1.25 1.02 1.16

0.30 0.04 0.57 0.04 0.39 0.52

0.77 1.09 1.06 1.58 1.30 1.14

0.78 1.13 1.08 1.51 1.25 1.16

1.03 1.03 1.46 0.98 1.30 1.23

0.42 0.15 0.62 −0.18 0.44 0.50

M. Plans et al. / Food Research International 56 (2014) 55–62 Table 6 Statistical descriptors for the NIR determinations in dried cooked beans. Property

PLS-factors R2cv

SECV R2pred RMSEP

SEP

RPD

RAP

Aroma Flavour Mealiness Seed-coat perception Seed-coat roughness Seed-coat brightness

3 6 3 1 6 3

0.71 0.65 0.77 1.02 1.07 0.97

0.67 0.71 0.83 1.28 1.05 0.97

1.19 1.62 1.90 1.16 1.55 1.47

0.41 0.81 0.83 0.35 0.53 0.69

0.44 0.63 0.76 0.42 0.66 0.56

0.31 0.70 0.81 0.26 0.59 0.55

0.77 0.71 0.81 1.26 1.22 0.99

from raw materials. For instance, NIRS analysis of rice showed improvement of the RPD for cooked samples (2.99 to 3.42) when compared to raw samples (1.1 to 2.4) for hardness, stickiness, and glossiness

A

attributes (Meullenet, Mauromoustakos, Horner, & Marks, 2002; Srisawas et al., 2007). In common beans, the molecules that have the greatest influence on the organoleptic properties undergo important transformations during soaking and cooking, which could explain the poor correlations obtained between the NIR and sensory characteristics for raw beans (Casañas, Pujolà, Bosch, Sánchez, & Nuez, 2002; Casañas et al., 2006; Pujolà, Farreras, & Casañas, 2007; Quenzer, Huffman, & Burns, 1978; Wang, Chang, & Grafton, 1988). Qualitative information regarding regions of the spectrum associated with explaining the highest variation in a calibration set is indicated by the loading weights. Frequencies of high variation reflect combinations of different chemical and physical phenomena (Bjorsvik & Martens, 1992; Kays, Windham, & Barton, 1998). The first loading plot

B

7

7 Flavour

Predicted Values

Predicted Values

Aroma

5

3

1

5

3

1 1

3

5

1

7

3

Reference Value

C

D

Predicted Values

Predicted Values

Seed-coat perception

7

5

3

3

5

7

5

3

1

9

1

3

Reference Value

F

9

5

7

Reference Value

Seed-coat roughness

7

Predicted Values

Predicted Values

E

7

7

Mealiness

1

5

Reference Value

9

1

59

5

3

1

9

Seed-coat brightness

7

5

3

1 1

3

5

Reference Value

7

9

1

3

5

7

9

Reference Value

Fig. 3. NIRS-predicted versus panel score of A) aroma, B) flavour, C) mealiness, D) seed-coat perception, E) seed-coat roughness and F) seed-coat brightness. Dried cooked beans. Validation: Black symbols. Calibration: white symbols. Target line in bold.

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M. Plans et al. / Food Research International 56 (2014) 55–62

for raw and dried cooked beans NIR models showed similar behaviour for the different sensory attributes with important bands centred at 1140 (aromatic C\H), 1186 (CH3), 1395 (CH2), 1410 and 1889 (O\H),

A

1920 (C_O), 2246 (N\H + NH3), 2284 (CH3), 2294 (N\H + C_O) and 2318 (CH2) nm (Fig. 4). Water region (1900 and 1410 nm) dominated the PLSR models using cooked undried common beans (Fig. 4).

B

0.4

0.4

1889

1889

0.3

0.3

0.2

2294 2246

0.1

1410

1186

0 1100

1300

1500

1700

-0.1

1900

2100

2300

1920

2500

PLSR Loadings

PLSR Loadings

0.2

1410

0.1

0 1100

1300

1500

1700

-0.1

2318

1140

-0.2

2246 2294

1186

1900

2100

2300

2500

1920 1140

-0.2

1395

1395

2284

-0.3

C

-0.3

Wavelenght (nm)

D

0.4

Wavelenght (nm)

0.3

1889

1889

0.3

0.2 2294

2294 2246

0.1

1410

1186

0 1100

1300

1500

1700

1900

2100

-0.1

2300

2500

PLSR Loadings

PLSR Loadings

0.2 0.1

0 1100

1410

1300

1500

1700

1900

2100

2300

2500

2318

-0.1

2318

1920 1140

2284

1920

1395

-0.2

-0.2

1395 2284

-0.3

E

-0.3

Wavelenght (nm)

F

0.4 0.3

Wavelenght (nm)

0.4 0.3

2246

2246 1889

1889 1410

0.2

PLSR Loadings

2294

0.1 0 1100 -0.1

1300

1500

1700

1900

2100

2300 2318

1140 2284

-0.2 -0.3 -0.4

1410 1920

1920

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PLSR Loadings

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Fig. 4. First partial least squares loading for each sensory trait: (A) aroma, (B) flavour, (C) mealiness, (D) seed-coat perception, (E) seed-coat roughness and (F) seed-coat brightness. Symbols: ··· raw, _____ undried cooked, - - - dried cooked.

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Comparison of the first loading plot between raw and cooked beans for aroma (Fig. 4A) and flavour (Fig. 4B) models showed that the bands at 1140, 1186, and 2246 nm had more weight using cooked than in raw spectra which can be associated to aromatic groups and protein changes during cooking process. Loadings for mealiness showed prevalence of the band at 1920 nm for cooked dried and raw beans attributed to CONH vibration modes in proteins structures (Fig. 4C). The 1920, 2246 and 2294 nm bands showed negative relation with seed-coat roughness (Fig. 4E) and seed-coat brightness (Fig. 4F) and these bands are associated to protein stretching and deformation vibration modes. In addition, seed-coat roughness (Fig. 4E) and seed-coat brightness (Fig. 4F) models showed similar loadings using raw and dried cooked beans spectra due to their high correlation coefficient (r = 0.86). Although cooking and drying of samples before spectra collection involve more work, NIRS analysis is less time-consuming than the tasting sessions. In fact, the use of trained panels for screening of large number of samples is cumbersome and expensive. NIRS analysis of cooked and dried samples can estimate some important sensory traits of the beans, enhancing the ability of breeding programmes to rapidly select for the most promising germplasm entries saving time and money to the breeder. Although, the proposed protocol involves destructive steps such as grounding and cooking of the seeds, only a fraction of the seeds produced by each plant are used in the analysis leaving the large majority for breeding purposes. 4. Conclusions NIRS enabled four sensory attributes (flavour, mealiness, seed-coat roughness and seed-coat brightness) to be roughly estimated simultaneously in dried cooked and grounded beans. In screening large collections of samples, this approach could be used to discard accessions that differ substantially from the ideotype in these sensory characteristics. This reduces the number of panel sessions required and makes it possible to carry out a selection that would otherwise be intractable. Although cooking, drying and grounding may increase the analysis time, the improvement of the NIR predictions using these approaches instead of raw uncooked beans makes it highly recommendable. To improve the robustness of the predictions, new accessions tested in each germplasm survey must be evaluated by tasting panels so they can be incorporated in the development and/or validation of the models. NIRS has the potential application for screening sensory attributes by breeding programmes or in quality control of beans for appellation schemes. Unfortunately, a model to estimate seed-coat perception was not found; developing a model for this attribute will presumably require working with NIR spectra registered only on seed-coat samples. Acknowledgements We thank the Spanish Instituto Nacional de Investigaciones Agrarias (Project RTA INIA 2011-00076-C02-02) for the funding for this study and Dr. Marcel Blanco (Universitat Autònoma de Barcelona) for his help with the NIR experimental measurements. References Almirall, A., Bosch, L., Romero del Castillo, R., Rivera, A., & Casañas, F. (2010). ‘Croscat’ common bean (Phaseolus vulgaris L.), a prototypical cultivar within the ‘Travella Brisa’ type. HortScience, 45(3), 432–433. Barnes, R., Dhanoa, M., & Lister, S. (1989). Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy, 43, 772–777. Bjorsvik, H. R., & Martens, H. (1992). Data analysis: Calibration of NIR instruments by PLS regression. In D. Burns, & E. Ciurczak (Eds.), Handbook of near-infrared analysis. NY: Dekker. Boeriu, C. G., Yuksel, D., de Vries, R. V. V., Stolle-Smits, T., & van Dijk, C. (1998). Correlation between near infrared spectra and texture profiling of steam cooked potatoes. Journal of Near Infrared Spectroscopy, 6, A291–A297.

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