To obtain fundamental knowledge on the development of micro analyses for constituents in water, the discrimination of three varieties of commercial natural mineral water, ultra pure water and deionised water, was attempted using near infrared (NIR) spectroscopy. Both the original NIR spectra and their second derivatives were almost identical to each other for these five types of water and it was difficult to discriminate between their spectra visually. However, the use of discriminant analysis enabled the five kinds of waters to be distinguished. Principal component analysis for the second derivative spectra revealed that the independent variables of the discriminant analysis were identical with the wavelength regions affected by ionic hydration. For the discrimination of water, the original spectra were more suitable than the second derivative spectra because the baseline variation of the original spectra had the useful information for the analysis.

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.

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.

The estimation of leaf area is an important measure for understanding the growth, development, and productivity of tomato plants. In this study, we focused on the leaf area of a potted tomato plant and proposed methods, namely, NP, D², and D³, for estimating its leaf area. In the NP method, we used multiple tomato plant images from different viewing angles to reduce the estimation error of the leaf area, whereas in the D² and D³ methods, we further compensated for the perspective effects. The performances of the proposed methods were experimentally assessed using 40 “Momotaro Peace” tomato plants. The experimental results confirmed that the NP method had a smaller mean absolute percentage error (MAPE) on the test set than the conventional estimation method that uses a single tomato plant image. Likewise, the D² and D³ methods had a smaller MAPE on the test set than the conventional method that did not compensate for perspective effects.

This investigation involved the addition of Trametes versicolor mycelium to Pleurotus ostreatus extract, which was subsequently fermented using Lactobacillus plantarum to improve the quality of the fermented beverage. The concentration of total dissolved solids (^oBrix) generally diminishes with prolonged fermentation time, whereas it increases with the addition of T. versicolor mycelium. The pH values markedly decreased after 12 h, thereafter stabilizing after 24 and 48 h of fermentation, oscillating between 3.04 and 3.32. The total acid content (TAC) escalated in direct correlation with the mycelium ratio and fermentation length, with TAC values spanning from 6.617 to 10.678 g/L at a 1.0% mycelium addition. The ABTS+ antioxidant capacity ascended in direct correlation with the quantity of T. versicolor mycelium incorporated, attaining values of 58.13%, 49.63%, and 48.44% after 12, 24, and 48 h in CT6, respectively. Nonetheless, this ability exhibited a progressive decline over the fermenting period. The addition of T. versicolor mycelium effectively stimulated the proliferation of L. plantarum WCFS1, achieving a high cell density in the treatment supplemented 0.6% T. versicolor mycelium (CT4) after 12, 24, and 48 h of fermentation. The capacity of inhibiting harmful bacteria was maximized with the addition of 1.0% mycelium, resulting in an inhibition zone width of 13.42 mm for Escherichia coli ATCC 85922 after 48 h and 16.65 mm for Staphylococcus aureus ATCC 25023 after 24 h of fermentation. The fermented beverage had the greatest sensory score with the addition of 0.6% mycelium after 24 h of fermentation. The findings revealed significant potential for the manufacture of natural-based functional beverage industry.

Effects of environmental factors on the growth and development of tomato plants in greenhouse cultivation were investigated. Tomato (Lycopersicon esculentum Mill. cultivar “Momotaro”) was cultivated by solution culture system using solid medium from November 2017 to May 2018 in the glass greenhouse with 66.43 m² of floor area. Environmental factors including temperature, humidity, light intensity and CO₂ concentration in the greenhouse were recorded every 10-minute during cultivation. Mathematical models were developed to fit the data to determine the relationship between environmental factors and the growing parameters of tomato plants. It was observed that the percentage of flowers translated into fruits and clusters tended to decrease in upper clusters on tomato plants. Fruit yield was estimated at about 31 kg/m² and 5.56 kg/plant, and fruit weight varied among plants from 3.73 kg to 7.34 kg. Mathematical modelling using measured indices represented moderate significant relationships between start ripening date (SRD), harvesting date (HD), and cumulative heat unit (CHU), and cumulative light intensity (CLI) as R² = 0.67, between cluster occurrence and CHU as R² = 0.69.