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Author: search.filters.author.Phanomsophon, ThitimaSubject: search.filters.subject.near-infrared spectroscopy

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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
    ;
    Phanomsophon, Thitima
    ;
    Maichoon, Pisut
    ;
    Pokhrel, Dharma Raj
    ;
    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<sup>−1</sup>. 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 R<sup>2</sup>, r<sup>2</sup>, 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<sup>−1</sup>, which included the amylose vibration band at 6834.0 cm<sup>−1</sup>, while the toughness model was from 6094.3 to 9403.8 cm<sup>−1</sup> and included the 6834.0 and 8316.0 cm<sup>−1</sup> vibration bands of amylose, which influenced the texture of the cooked rice. The PLS regression models for hardness and toughness had the r<sup>2</sup> 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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    Non-Destructive Quality Evaluation of Tropical Fruit (Mango and Mangosteen) Purée Using Near-Infrared Spectroscopy Combined with Partial Least Squares Regression
    (2022-12-01)
    Pornchaloempong, Pimpen
    ;
    Sharma, Sneha
    ;
    Phanomsophon, Thitima
    ;
    Srisawat, Kraisuwit
    ;
    Inta, Wasan
    Mango and mangosteen are commercially important tropical fruits with a short shelf life. Fruit processing is one of the alternatives to extend the shelf life of these fruits. Purée is one of the processed products of fresh fruit. In this research, the quality of mango and mangosteen purée was analyzed. Titratable acidity (TA) and total soluble solids (TSS) were predicted using non-destructive near-infrared (NIR) spectroscopy. A partial least squares regression (PLSR) model was developed based on the NIR spectra with a wavelength ranging from 800 to 2500 nm. The PLSR model returned a coefficient of determination (r<sup>2</sup>) and a ratio of prediction to deviation (RPD) of 0.955 and 4.7 for TSS, and 0.784 and 2.2 for TA, in the mango purée. Similarly, the best model was selected for the TSS prediction in the mangosteen purée through PLSR, with an r<sup>2</sup>, a root mean square error of cross-validation (RMSECV), and RPD of 0.799, 0.3% malic acid, and 2.2, respectively. The results show the possible application of NIR spectroscopy in the product processing line, although a larger number of samples with wide variation in future studies are needed as an input to update the model, in order to obtain a more robust model.