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Modeling Textural Properties of Cooked Germinated Brown Rice Using the near-Infrared Spectra of Whole Grain
(2023-12-01)
Kaewsorn, Kannapot
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Phanomsophon, Thitima
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Maichoon, Pisut
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Pokhrel, Dharma Raj
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Pornchaloempong, Pimpen
If a non-destructive and rapid technique to determine the textural properties of cooked germinated brown rice (GBR) was developed, it would hold immense potential for the enhancement of the quality control process in large-scale commercial rice production. We combined the Fourier transform near-infrared (NIR) spectral data of uncooked whole grain GBR with partial least squares (PLS) regression and an artificial neural network (ANN) for an evaluation of the textural properties of cooked germinated brown rice (GBR); in addition, data separation and spectral pretreatment methods were investigated. The ANN was outperformed in the evaluation of hardness by a back extrusion test of cooked GBR using the smoothing combined with the standard normal variate pretreated NIR spectra of 188 whole grain samples in the range of 4000–12,500 cm−1. The calibration sample set was separated from the prediction set by the Kennard–Stone method. The best ANN model for hardness, toughness, and adhesiveness provided R2, r2, RMSEC, RMSEP, Bias, and RPD values of 1.00, 0.94, 0.10 N, 0.77 N, 0.02 N, and 4.3; 1.00, 0.92, 1.40 Nmm, 9.98 Nmm, 1.6 Nmm, and 3.5; and 0.97, 0.91, 1.35 Nmm, 2.63 Nmm, −0.08 Nmm, and 3.4, respectively. The PLS regression of the 64-sample KDML GBR group and the 64-sample GBR group of various varieties provided the optimized models for the hardness of the former and the toughness of the latter. The hardness model was developed by using 5446.3–7506 and 4242.9–4605.4 cm−1, which included the amylose vibration band at 6834.0 cm−1, while the toughness model was from 6094.3 to 9403.8 cm−1 and included the 6834.0 and 8316.0 cm−1 vibration bands of amylose, which influenced the texture of the cooked rice. The PLS regression models for hardness and toughness had the r2 values of 0.85 and 0.82 and the RPDs of 2.9 and 2.4, respectively. The ANN model for the hardness, toughness, and adhesiveness of cooked GBR could be implemented for practical use in GBR production factories for product formulation and quality assurance and for further updating using more samples and several brands to obtain the robust models.
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Evaluation of Precision and Sensitivity of Back Extrusion Test for Measuring Textural Qualities of Cooked Germinated Brown Rice in Production Process
(2023-08-01)
Kaewsorn, Kannapot
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Maichoon, Pisut
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Pornchaloempong, Pimpen
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Krusong, Warawut
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Sirisomboon, Panmanas
The textural qualities of cooked rice may be understood as a dominant property and indicator of eating quality. In this study, we evaluated the precision and sensitivity of a back extrusion (BE) test for the texture of cooked germinated brown rice (GBR) in a production process. BE testing of the textural properties of cooked GBR rice showed a high precision of measurement in hardness, toughness and stickiness tests which indicated by the repeatability and reproductivity test but the sensitivity indicated by coefficient of variation of the texture properties. The findings of our study of the effects on cooked GBR texture of different soaking and incubation durations in the production of Khao Dawk Mali 105 (KDML 105) GBR, as measured by BE testing, confirmed that our original protocol for evaluation of the precision and sensitivity of this texture measurement method. The coefficients of determination (R2) of hardness, toughness and stickiness tests and the incubation time at after 48 hours of soaking were 0.82, 0.81 and 0.64, respectively. The repeatability and reproducibility of reliable measurements, which have a low standard deviation of the greatest difference between replicates, are considered to indicate high precision. A high coefficient of variation where relatively wide variations in the absolute value of the property can be detected indicates high sensitivity when small resolutions can be detected, and vice versa. The sensitivity of the BE tests for stickiness, toughness and hardness all ranked higher, in that order, than the sensitivity of the method for adhesiveness, which ranked lowest. The coefficients of variation of these texture parameters were 31.26, 20.59, 19.41 and 18.72, respectively. However, the correlation coefficients among the texture properties obtained by BE testing were not related to the precision or sensitivity of the test. By obtaining these results, we verified that our original protocol for the determination of the precision and sensitivity of food texture measurements which was successfully used for GBR texture measurement.
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Nondestructive estimation of maturity and textural properties on tomato 'Momotaro' by near infrared spectroscopy
(2012-10-01)
Sirisomboon, Panmanas
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Tanaka, Munehiro
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Kojima, Takayuki
;
Williams, Phil
Near infrared spectroscopy offers the possibility to classify and predict the internal quality of fruits and vegetables. The objective of this study was to evaluate the ability of near infrared spectroscopy to classify the maturity level and to predict textural properties of tomatoes variety "Momotaro". Principal component analysis (PCA) and Soft independent modeling of class analogy (SIMCA) were used to distinguish among different maturities (mature green, pink and red). Partial least squares (PLS) regression was used to estimate textural properties, alcohol insoluble solids and soluble solids content of the tomatoes. The PCA calibration model with mean normalization pretreatment spectra of mature green tomatoes, gave the highest distinguishability (96.85%). It could classify 100.00% of red and pink tomatoes. The SIMCA model could not give better accuracy in maturity classification than individual PCA models. Among the textural parameters measured, the bioyield force from the puncture test with the near infrared (NIR) spectra (between 1100 and 1800 nm) pretreated by multiplicative scatter correction (MSC) had the highest correlation coefficient between NIR predicted and reference values (r = 0.95) and lowest standard error of prediction (SEP = 0.35 N) and bias of 0.19 N. The ratio of standard deviation of reference data of prediction set to standard error of prediction (RPD) was 2.71. In the case of Momotaro tomato, NIR spectroscopy by using PLS regression could not predict alcohol insoluble solids in fresh weight accurately but could predict soluble solids content well with r of 0.80, SEP of 0.210 %Brix and bias of 0.022 %Brix. © 2012 Elsevier Ltd. All rights reserved.
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Evaluation of tomato textural mechanical properties
(2012-08-01)
Sirisomboon, Panmanas
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Tanaka, Munehiro
;
Kojima, Takayuki
The texture of fresh tomatoes (Lycopersicum esculentum Mill.; 'Momotaro') at three different stage of ripeness (mature green, pink, and red) was intensively evaluated. The double-cycle load plate compression test showed that initial firmness, average firmness, energy absorption, deformation ratio, and relaxation ratio were sensitive textural parameters for identifying the maturity stages. From a puncture test, initial firmness, average firmness, apparent modulus of elasticity, rupture force, toughness, and deformation at the rupture point were sensitive to the maturity stages. The mature unripe tomato deformed linearly as a function of force; hence, the firmness was constant. However, the fully ripe tomato firmness increased as a function of applied force. The fruit strain increased during ripening, and it was independent of fruit size. The unripe tomato was more elastic than the ripe one. The peel at the mature green stage contributed approximately 70% of the firmness of the fruit and approximately 90% at the pink stage and red stage. The rupture force by the puncture test (traditionally, peak force) was correlated well with other textural parameters, which indicated that it could be used as a firmness representative parameter, as has been used by many researchers. The degree of elasticity was a better indicator for elasticity than the relaxation ratio. © 2012 Elsevier Ltd. All rights reserved.
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Evaluation of pectin constituents of Japanese pear by near infrared spectroscopy
(2007-01-01)
Sirisomboon, Panmanas
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Tanaka, Munehiro
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Fujita, Shuji
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Kojima, Takayuki
Japanese pears (Pyrus serotina Rehder var. culta 'Housui') collected in 1997 and 1998 were measured for their pectin constituents including alcohol insoluble solids, water soluble pectin, oxalate soluble pectin, non-soluble pectin and total pectin. Near infrared (NIR) spectra (1100-2500 nm) were measured within the range of at 2 nm intervals. The NIR spectra of intact Japanese pear were measured by fiber optics in interactance mode and the spectra of juice were measured by diffuse trans-reflectance. The spectral data used were raw spectra and their second derivative. By using multiple linear regression, calibration equations developed from the intact fruit spectra and juice spectra, the alcohol insoluble solids in the fresh weight (AIS in the FW) and the oxalate soluble pectin content in the alcohol insoluble solids (OSP in the AIS) were accurately predicted (For intact fruit spectra: R = 0.93, SEP = 0.62 for AIS in the FW, and R = 0.95, SEP = 8.48 for OSP in the AIS; For juice spectra: R = 0.93, SEP = 0.63 for AIS in the FW and R = 0.91, SEP = 7.93 for OSP in the FW). In addition, the equations from the juice spectra could be used to predict the water soluble pectin in the alcohol insoluble solids (WSP in the AIS), and the total pectin in the alcohol insoluble solids (TP in the AIS) (R = 0.91, SEP = 1.41 for WSP in the AIS and R = 0.94, SEP = 11.52 for TP in the AIS). The NIR models developed with the data collected in 1998 were not able to predict the 1997 data. This study showed that near infrared spectroscopy has potential to measure the pectin constituents of the Japanese pear. © 2005 Elsevier Ltd. All rights reserved.

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