Determination of soluble solid content and acidity of loquats based on FT-NIR spectroscopy

Determination of soluble solid content and acidity of loquats based on FT-NIR spectroscopy first-author corresp ‡Corresponding Author ‡Corresponding Author †E-mail:jpli@zju.edu.cn †E-mail:jpli@zju.edu.cn jpli@zju.edu.cn College of Biosystem Engineering and Food Science, Zhejiang University, Hangzhou 310029, China College of Biosystem Engineering and Food Science, Zhejiang University, Hangzhou 310029, China The near infrared (NIR) spectroscopy technique has been applied in many fields because of its advantages of simple preparation, fast response, and non-destructiveness. We investigated the potential of NIR spectroscopy in diffuse reflectance mode for determining the soluble solid content (SSC) and acidity (pH) of intact loquats. Two cultivars of loquats (Dahongpao and Jiajiaozhong) harvested from two orchards (Tangxi and Chun’an, Zhejiang, China) were used for the measurement of NIR spectra between 800 and 2500 nm. A total of 400 loquats (100 samples of each cultivar from each orchard) were used in this study. Relationships between NIR spectra and SSC and acidity of loquats were evaluated using partial least square (PLS) method. Spectra preprocessing options included the first and second derivatives, multiple scatter correction (MSC), and the standard normal variate (SNV). Three separate spectral windows identified as full NIR (800~2500 nm), short NIR (800~1100 nm), and long NIR (1100~2500 nm) were studied in factorial combination with the preprocessing options. The models gave relatively good predictions of the SSC of loquats, with root mean square error of prediction (RMSEP) values of 1.21, 1.00, 0.965, and 1.16 °Brix for Tangxi-Dahongpao, Tangxi-Jiajiaozhong, Chun’an-Dahongpao, and Chun’an-Jiajiaozhong, respectively. The acidity prediction was not satisfactory, with the RMSEP of 0.382, 0.194, 0.388, and 0.361 for the above four loquats, respectively. The results indicate that NIR diffuse reflectance spectroscopy can be used to predict the SSC and acidity of loquat fruit. Near infrared (NIR) spectroscopy Loquats Soluble solid content (SSC) Acidity Partial least square (PLS) Modeling Spectra preprocessing INTRODUCTION 1 Fruit internal quality is an important factor influencing marketing transportation and storage requirements. Determination of fruit internal quality using destructive and invasive methods such as chemical or texture analysis is more complicated than external quality detection (like shape, color, size, etc.) (Hsieh and Lee, 2005 2005 ). It is impossible to apply these methods for on-line assessment. Near infrared (NIR) technique is a fast and nondestructive approach, which can obtain internal information of a product by measuring the amount of light absorbed by functional groups over the NIR spectral range without or with little sample preparation. NIR region was first discovered by Herschel ( 1800 1800 ) as early as 1800; however, it had been ignored for 50 years in the first half of the 19th century. Although the earliest applications of NIR spectroscopy for agricultural and food samples were reported in the 1950s by the group headed by Norris (Mercurl et al., 1957 1957 ), it was not until 1970s that NIR analyzers were developed. From the later 1980s, NIR spectroscopy has gained wide acceptance by its advantages over other analytical techniques and has been used in various fields. By now, many studies have reported that NIR spectroscopy techniques can nondestructively detect internal qualities, such as soluble solid content (SSC), valid acidity, dry matter, and firmness of fruit. For example, Ventura et al. ( 1998 1998 ) used NIR method (at 810~999 nm) based on a dual-beam, fiber-optic portable spectrometer to determine SSC in Golden Delicious and Jonagold apples nondestructively. They used multiple linear regression (MLR) models for prediction and obtained standard error of prediction (SEP) of 1.14 °Brix. Lammertyn et al.( 2000 2000 ) established the relationship between the reflectance spectra (at 880~1650 nm) and the SSC of apples by means of the partial least square (PLS) technique. Lu and Ariana ( 2002 2002 ) investigated an NIR sensing technique in interactant mode for rapid acquisition of spectral information to predict the sugar content and possibly firmness of Empire and Red Delicious apples. Others (Kawano et al., 1993 1993 ; Slaughter, 1995 1995 ; Peiris et al., 1999 1999 ; McGlone et al., 2002 2002 ; Ying et al., 2005 2005 ; Liu and Ying, 2005 2005 ; Liu et al., 2007 2007 ; Ariana et al., 2006 2006 ; Fu et al., 2008 2008 ) have studied other fruits, such as cucumbers, mandarins, kiwifruits, peaches, and pears, based on NIR spectroscopy technique. Loquat ( Eriobotrya japonica Lindl) has been cultivated in China for more than 2000 years and now is mainly planted in Zhejiang, Fujian and Jiangsu Provinces. Different varieties have different shape, color, size, etc. For loquats, external defect and size were two main parameters used for classifying by farmers. However, it is difficult for them to evaluate the internal quality. SSC and acidity are two important factors that affect loquat quality and strongly influence its taste and market value. It is important to develop a nondestructive approach to measure these qualities for loquat quality control and classification. So far, no studies have been reported on using NIR techniques for measuring the SSC and acidity of loquats. The objective of this research was to investigate the potential of NIR spectroscopy in diffuse reflectance mode for determining the SSC and acidity (pH) of intact loquats. We also examined the influence of spectra preprocessing, such as derivative, multiple scatter correction (MSC), and standard normal variate, on NIR modeling performance for SSC and acidity prediction in three wavelength regions: full NIR region (800~2500 nm), short NIR region (800~1100 nm), and long NIR region (1100~2500 nm). MATERIALS AND METHODS 1 Loquat samples 2 Mature Dahongpao and Jiajiaozhong loquats were harvested from two commercial orchards in Tangxi and Chun’an, two of the main loquat production areas in Zhejiang Province, China. A total of 400 loquats (100 samples of each cultivar from each orchard) were manually picked during commercial harvest period, transported immediately to the laboratory, and stored in a cooler (4 °C) for study. Before test, samples of one cultivar from one orchard were taken out from the cooler and maintained at room temperature (25 °C) for 24 h to equilibrate to the experimental condition. The measurements including spectra collection and quality analysis of these samples were carried out on the same day. Spectral data acquisition 2 NIR spectra of intact loquats were acquired in diffuse reflectance mode. A custom-designed laboratory system was utilized, which consisted of a fruit holder/light collection fixture, a Fourier transform infrared (FT-IR) spectrometer (Thermo Electron Corp., Madison, Wisc., USA), a bifurcated optical fiber, and a computer. The fiber optic probe was enclosed in a 16-mm-diameter stainless steel cylindrical tube, with both light source beams and receptor beams enclosed in it randomly. The spectrometer had a wide band light source (50 W quartz tungsten halogen lamp), an interferometer, and a thermal electric cooled InGaAs detector with a spectral range of 800~2630 nm. Spectral data acquisition and storage were achieved with the computer running software OMNIC v6.0 (Thermo Electron Corp., Madison, Wisconsin, USA). Each reflectance spectrum was accumulated by averaging 64 repetitive scans at a single location with resolution of 16 cm −1 and then transformed to absorption log(1/ R ), where R is the ratio of light intensity reflected from the fruit to light intensity reflected from the Teflon block. Loquats were positioned centrally and steadily upon the fruit holder, with stem-calyx axis horizontally oriented. For each fruit, three locations were selected around the equator (about 120° apart). Before sample spectra collection, the standard reference spectrum was obtained by placing a Teflon block on the fruit holder and measuring the intensity of reflected light. SSC and acidity measurement 2 According to the China Standard ( 1990 1990 ) for SSC measurement in fruit, the flesh of each fruit (by removing the peel and stone) was used for extracting juice with a commercial juice extractor. Filtered supernatant juice was used for determination of SSC and acidity. SSC was measured by a digital refractometer (ATAGO PR-101, Tokyo, Japan). Following the China Standard ( 1989 1989 ) for valid acidity measurement in fruit, acidity was detected by a pH meter (SJ-4A, Exact Instrumentation Co., Shanghai, China). Data processing and analysis 2 NIR calibration and validation were conducted using a random split of the fruit of each variety: a calibration set (70 samples) and a validation set (30 samples). The principal modeling tool used was the PLS method (TQ Analyst v6.1, Thermo Electron Corp., Madison, Wisconsin, USA). Spectra preprocessing options included the first and second derivatives, MSC, and the standard normal variate (SNV). Three separate spectral windows identified as full NIR (800~2500 nm), short NIR (800~1100 nm), and long NIR (1100~2500 nm) were studied in factorial combination with the preprocessing options. The optimal combination was chosen on the basis of the minimum root mean square error of cross validation (RMSECV), which was also used to choose the number of factors in the model, up to a maximum of 20. Once completed, the calibration model was applied to the validation set, and the regression analysis between actual SSC/acidity and predicted SSC/acidity was used to judge the predictive performance of the model using the statistic correlation coefficient R and the root mean square error of prediction (RMSEP). RESULTS AND DISCUSSION 1 SSC and acidity of samples 2 Table 1 1 summarizes the maximum, minimum, means and standard deviations of four datasets (two varieties from two orchards: Tangxi-Dahongpao, Tangxi-Jiajiaozhong, Chun’an-Dahongpao, and Chun’an-Jiajiaozhong). On average, Dahongpao loquats were much sweeter and less sour (higher pH value) than Jiajiaozhong, as indicated by the average SSC and acidity. Dahongpao loquats had more variations in both SSC and acidity than Jiajiaozhong did. As shown in Table 1 1 , Chun’an-Dahongpao loquats had the highest SSC and pH value (SSC=15.06 °Brix, pH=4.59), Chun’an-Jiajiaozhong had the lowest SSC (10.80 °Brix), and Tangxi-Jiajiaozhong had lowest pH (3.61). SD : standard deviation 2 Dataset 4 SSC (°Brix) 4 Acidity (pH) Maximum Minimum Average SD Maximum Minimum Average SD Tangxi-Dahongpao 19.80 5.70 14.07 2.61 5.07 3.22 4.32 0.41 Tangxi-Jiajiaozhong 17.40 8.40 12.79 1.65 4.26 3.21 3.61 0.18 Chun’an-Dahongpao 20.10 9.20 15.06 2.10 5.44 3.43 4.59 0.43 Chun’an-Jiajiaozhong 16.80 6.50 10.80 2.09 5.29 3.24 4.33 0.36 Spectra of samples 2 Four spectra corresponding to the four datasets are shown in Fig. 1 1 . Each spectrum was the average of 100 spectra in each dataset. The diffuse reflectance spectra curves were quite smooth across the entire NIR spectral region and were characterized by five broad absorption peaks. Absorption band at 1940 nm is related to combination band of water. Absorption bands around 1450 nm and 970 nm are related to the O–H first and second overtones of water, respectively. And bands at 1790 nm and 1190 nm are related to the C–H second and third overtones. SSC predictions 2 The preprocessing of the diffuse reflectance spectra, including derivatives, MSC and SNV correction, and the number of factors (from 1 to 20) with PLS models were taken into account. The performance of the models was evaluated by leave-one-out cross validation, that is, the minimum RMSECV and maximum correlation coefficient of cross validation ( R cv ). The RMSECV with different number of factors of PLS quantitative models for SSC prediction is shown in Fig. 2 2 . For each dataset, the RMSECV decreased with an increasing number of factors until it reached a certain number. The optimum number of calibration factors was selected based on the minimum RMSECV. The results of models based on original spectra in full wavelength range (800~2500 nm) were always better than those of models based on derivative spectra (the 1st derivative and 2nd derivative spectra) in short NIR (800~1100 nm) and long NIR (1100~2500 nm). The calibration, cross validation, and prediction results of SSC from the PLS models for Dahongpao and Jiajiaozhong loquats from two orchards are shown in Fig. 3 3 and Table 2 2 . The modeling results were better based on original spectra compared with those based on spectra after MSC and SNV correction, except for Tangxi-Jiajiaozhong variety. (a) Chun’an-Dahongpao; (b) Chun’an-Jiajiaozhong; (c) Tangxi-Dahongpao; (d) Tangxi-Jiajiaozhong Abbreviations are number of samples in the calibration ( N c ) and prediction ( N p ) sets, number of factors ( N F ) used, root mean square error of cross validation ( RMSECV ) and prediction ( RMSEP ), and correlation coefficient of cross validation ( R cv ) 2 Sample sets 2 Preprocessing 2 N F 3 Cross validation 2 Prediction N c RMSECV R cv N p RMSEP Tangxi-Dahongpao − 11 70 1.180 0.901 30 1.210 Tangxi-Jiajiaozhong MSC 5 70 1.180 0.717 30 1.000 Chun’an-Dahongpao − 14 70 1.110 0.860 30 0.965 Chun’an-Jiajiaozhong − 9 70 0.923 0.909 30 1.160 The correlation coefficient and RMSEC of PLS calibration models for SSC of Tangxi-Dahongpao, Tangxi-Jiajiaozhong, Chun’an-Dahongpao, and Chun’an-Jiajiaozhong were 0.991, 0.361; 0.842, 0.911; 0.998, 0.122; and 0.976, 0.487, respectively (Fig. 3 3 ). According to cross validation (Table 2 2 ), the model for Chun’an-Jiajiaozhong obtained the best results, with R cv =0.909, and the model for Tangxi-jiajiaozhong was the worst, with R cv =0.717. Acidity predictions 2 The same methods were applied to acidity prediction. The calibration, cross validation, and prediction results of acidity from the PLS models for Dahongpao and Jiajiaozhong loquats from two orchards are shown in Fig. 4 4 and Table 3 3 . Original spectra in full wavelength range (800~2500 nm) were also better for modeling than derivative spectra (the 1st derivative and 2nd derivative spectra) in short NIR (800~1100 nm) and long NIR (1100~2500 nm). The modeling results were better based on spectra after MSC (Tangxi-Jiajiaozhong, Chun’an-Dahongpao and Chun’an-Jiajiaozhong) and SNV (Tangxi-Dahongpao) correction compared with those based on original spectra. (a) Chun’an-Dahongpao; (b) Chun’an-Jiajiaozhong; (c) Tangxi-Dahongpao; (d) Tangxi-Jiajiaozhong Abbreviations are number of samples in the calibration ( N c ) and prediction ( N p ) sets, number of factors ( N F ) used, root mean square error of cross validation ( RMSECV ) and prediction ( RMSEP ), and correlation coefficient of cross validation ( R cv ) 2 Sample sets 2 Preprocessing 2 N F 3 Cross validation 2 Prediction N c RMSECV R cv N p RMSEP Tangxi-Dahongpao SNV 6 70 0.361 0.537 30 0.382 Tangxi-Jiajiaozhong MSC 5 70 0.137 0.609 30 0.194 Chun’an-Dahongpao MSC 3 70 0.407 0.374 30 0.388 Chun’an-Jiajiaozhong MSC 6 70 0.335 0.494 30 0.361 The correlation coefficient and RMSEC of PLS calibration models for acidity of Tangxi-Dahongpao, Tangxi-Jiajiaozhong, Chun’an-Dahongpao, and Chun’an-Jiajiaozhong were 0.820, 0.237; 0.809, 0.099; 0.531, 0.367; and 0.687, 0.273, respectively (Fig. 4 4 ). According to cross validation (Table 3 3 ), the model for Tangxi-Jiajiaozhong obtained the best results, with R cv =0.609; and the model for Chun’an-Dahongpao was the worst, with R cv =0.374. The model results for acidity prediction were much worse than those for SSC prediction. CONCLUSION 1 From the results, it can be seen that there were relatively good correlations between measured SSC and predicted SCC by NIR spectra for the two varieties of loquats from two orchards; however, the results for acidity prediction were not as good as SSC. The results show that the modeling results were better for the data in full NIR spectral range (800~2500 nm) than that in short NIR (800~1100 nm) and long NIR (1100~2500 nm) spectra regions. We also found that derivative spectra were not as good as original spectra for modeling. The application of MSC or SNV correction processing can improve the results of acidity prediction. Models for SSC predictions yielded better results than those for acidity predictions, and the correlation coefficients between NIR spectra and SSC were much higher than those between NIR spectra and acidity. References PD Ariana , R Lu , ED Guyer . Near-infrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers . Computers and Electronics in Agriculture , 2006 . 53 ( 1 ): 60 - 70 . DOI: 10.1016/j.compag.2006.04.001 http://doi.org/10.1016/j.compag.2006.04.001 . Near-infrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers China Standard GB 10468-89, Detection of pH Value in Fruit and Vegetables , 1989 . : Beijing Standard Press of China , (in Chinese) . GB 10468-89, Detection of pH Value in Fruit and Vegetables China Standard GB 12295-1990, Detection of Soluble Solids Content in Fruit and Vegetables—Refractometer Method , 1990 . : Beijing Standard Press of China , (in Chinese) . GB 12295-1990, Detection of Soluble Solids Content in Fruit and Vegetables—Refractometer Method XP Fu , YB Ying , Y Zhou , LJ Xie , HR Xu . Application of NIR spectroscopy for firmness evaluation of peaches . Journal of Zhejiang University SCIENCE B , 2008 . 9 ( 7 ): 552 - 557 . 18600785 DOI: 10.1631/jzus.B0720018 http://doi.org/10.1631/jzus.B0720018 . Application of NIR spectroscopy for firmness evaluation of peaches W Herschel . Investigation of the powers of the prismatic colors to heat and illuminate objects . Philosophical Transactions of the Royal Society of London (1776-1886) , 1800 . 90 255 - 283 . DOI: 10.1098/rstl.1800.0014 http://doi.org/10.1098/rstl.1800.0014 . Investigation of the powers of the prismatic colors to heat and illuminate objects C Hsieh , Y Lee . Applied visible/near-infrared spectroscopy on detection the sugar content and hardness of pearl guava . Applied Engineering in Agriculture , 2005 . 21 ( 6 ): 1039 - 1046 . . Applied visible/near-infrared spectroscopy on detection the sugar content and hardness of pearl guava S Kawano , T Fujiwara , M Iwamoto . Nondestructive determination of sugar content in satsuma mandarin using near infrared (NIR) transmittance . J Japan Soc Hort Soc , 1993 . 62 ( 2 ): 465 - 470 . . Nondestructive determination of sugar content in satsuma mandarin using near infrared (NIR) transmittance J Lammertyn , A Peirs , JD Baerdemaeker , B Nicolai . Light penetration properties of NIR radiation in fruit with respect to nondestructive quality assessment . Postharvest Biology and Technology , 2000 . 18 ( 2 ): 121 - 132 . DOI: 10.1016/S0925-5214(99)00071-X http://doi.org/10.1016/S0925-5214(99)00071-X . Light penetration properties of NIR radiation in fruit with respect to nondestructive quality assessment Y Liu , Y Ying . An optical system for measurement of internal pear quality using near-infrared spectroscopy . Journal of Optical Engineering , 2005 . 47 ( 7 ): 1 - 5 . . An optical system for measurement of internal pear quality using near-infrared spectroscopy Y Liu , L Luo , A Ouyang . Visible transmittance techniques fresh navel orange juices for rapid analysis of sugar content . Spectroscopy and Spectral Analysis , 2007 . 27 ( 3 ): 569 - 572 . (in Chinese) 17554924 . Visible transmittance techniques fresh navel orange juices for rapid analysis of sugar content R Lu , D Ariana . A near-infrared sensing technique for measuring internal quality of apple fruit . Applied Engineering in Agriculture , 2002 . 18 ( 5 ): 585 - 590 . . A near-infrared sensing technique for measuring internal quality of apple fruit VA McGlone , RB Jordan , PJ Martinsen . Vis/NIR estimation at harvest of pre- and post-storage quality indices for ‘Royal Gala’ apple . Postharvest Biology and Technology , 2002 . 25 ( 2 ): 135 - 145 . DOI: 10.1016/S0925-5214(01)00180-6 http://doi.org/10.1016/S0925-5214(01)00180-6 . Vis/NIR estimation at harvest of pre- and post-storage quality indices for ‘Royal Gala’ apple AJ Mercurl , JE Thomson , JD Rowan , KH Norris . Use of the automatic green-rot detector to improve the quality of liquid egg . Food Technol , 1957 . 11 374 - 377 . . Use of the automatic green-rot detector to improve the quality of liquid egg KHS Peiris , GG Dull , RG Leffler , SJ Kays . Spatial variability of soluble solids or dry matter content within individual fruits, bulbs, or tubers: implications for the development and use of NIR spectrometric techniques . HortScience , 1999 . 43 114 - 118 . . Spatial variability of soluble solids or dry matter content within individual fruits, bulbs, or tubers: implications for the development and use of NIR spectrometric techniques DC Slaughter . Nondestructive determination of internal quality in peaches and nectarines . Transactions of the ASAE , 1995 . 38 ( 2 ): 617 - 623 . . Nondestructive determination of internal quality in peaches and nectarines M Ventura , AD Jager , HD Putter , FP Roelofs . Nondestructive determination of soluble solid in apple fruit by near infrared spectroscopy (NIRS) . Postharvest Biology and Technology , 1998 . 14 ( 1 ): 21 - 27 . DOI: 10.1016/S0925-5214(98)00030-1 http://doi.org/10.1016/S0925-5214(98)00030-1 . Nondestructive determination of soluble solid in apple fruit by near infrared spectroscopy (NIRS) Y Ying , Y Liu , J Wang , X Fu , Y Li . Fourier transform near-infrared determination of total soluble solids and available acid in intact peaches . Transaction of ASAE , 2005 . 48 ( 1 ): 229 - 234 . . Fourier transform near-infrared determination of total soluble solids and available acid in intact peaches 10.1631/jzus.B0820097 Project supported by the National Natural Science Foundation of China (No. 30825027) and the National Key Technology R&D Program of China (No. 2006BAD11A12) 2008-03-26 2009-01-05 2008-10-08 2022-01-05 Copyright © 2009, Journal of Zhejiang University Science 2009 Journal of Zhejiang University-SCIENCE B (Biomedicine and Biotechnology) 02 10