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Pomelo fruit pulp mainly is consumed fresh and with very little processing, and its peels are discarded as biological waste, which can cause the environmental problems. The peels contain several bioactive chemical compounds, especially essential oils (EOs). The content of a specific EO is important for the extraction process in industry and in research units such as breeding research. The explanation of the biosynthesis pathway for EO generation and change was included. The chemical bond vibration affected the prediction of EO constituents was comprehensively explained by regression coefficient plots and x-loading plots. Visible and near-infrared spectroscopy (VIS/NIRS) is a prominent rapid technique used for fruit quality assessment. This research work was focused on evaluating the use of VIS/NIRS to predict the composition of EOs found in the peel of the pomelo fruit (Citrus maxima (J. Burm.) Merr. cv Kao Nam Pueng) following storage. The composition of the peel oil was analyzed by gas chromatography–mass spectrometry (GC-MS) at storage durations of 0, 15, 30, 45, 60, 75, 90, 105 and 120 days (at 10 °C and 70% relative humidity). The relationship between the NIR spectral data and the major EO components found in the peel, including nootkatone, geranial, β-phellandrene and limonene, were established using the raw spectral data in conjunction with partial least squares (PLS) regression. Preprocessing of the raw spectra was performed using multiplicative scatter correction (MSC) or second derivative preprocessing. The PLS model of nootkatone with full MSC had the highest correlation coefficient between the predicted and reference values (r = 0.82), with a standard error of prediction (SEP) of 0.11% and bias of 0.01%, while the models of geranial, β-phellandrene and limonene provided too low r values of 0.75, 0.75 and 0.67, respectively. The nootkatone model is only appropriate for use in screening and some other approximate calibrations, though this is the first report of the use of NIR spectroscopy on intact fruit measurement for its peel EO constituents during cold storage. [ABSTRACT FROM AUTHOR]

This research comparatively investigates different mangosteen pericarp processing schemes. The experimental pericarp processing schemes were hot air drying (HAD; control), quick freezing/HAD (QF + HAD), slow freezing/HAD (SF + HAD), and slow freezing/freeze-drying (SF + FD). For freezing, the QF temperature was −38 °C for 2 h and that of SF was −25 °C for 2 weeks. For drying, the HAD temperature was 60 °C for 7 h. In the FD process, the primary and secondary temperatures were −20 °C and 50 °C for 48 h. The experimental results showed that the freezing method (i.e., QF and SF) affected the physical properties (moisture content, water activity, and color) of dried mangosteen pericarp. The antioxidant activities (DPPH and ABTS) of the SF + HAD scheme (28.20 and 26.86 mg Trolox/g DW of mangosteen pericarp) were lower than the SF + FD scheme (40.68 and 41.20 mg Trolox/g DW of mangosteen pericarp). The α-mangostin contents were 82.3 and 78.9 mg/g DW of mangosteen pericarp for FD and HAD, respectively; and the corresponding TPC were 1065.57 and 783.24 mg GAE/g DW of mangosteen pericarp. The results of this study suggest that the drying process had a negligible effect on bioactive compounds. Essentially, the SF + HAD technology is the most operationally and economically viable scheme to process mangosteen pericarp. [ABSTRACT FROM AUTHOR]

Background: This study explored the utilization of luffa sponge (LS) in enhancing acetification processes. LS is known for having high porosity and specific surface area, and can provide a novel means of supporting the growth of acetic acid bacteria (AAB) to improve biomass yield and acetification rate, and thereby promote more efficient and sustainable vinegar production. Moreover, the promising potential of LS and luffa sponge coated with κ-carrageenan (LSK) means they may represent effective alternatives for the co-production of industrially valuable bioproducts, for example bacterial cellulose (BC) and acetic acid. Methods: LS and LSK were employed as adsorbents for Acetobacter pasteurianus UMCC 2951 in a submerged semi-continuous acetification process. Experiments were conducted under reciprocal shaking at 1 Hz and a temperature of 32 °C. The performance of the two systems (LS-AAB and LSK-AAB respectively) was evaluated based on cell dry weight (CDW), acetification rate, and BC biofilm formation. Results: The use of LS significantly increased the biomass yield during acetification, achieving a CDW of 3.34 mg/L versus the 0.91 mg/L obtained with planktonic cells. Coating LS with κ-carrageenan further enhanced yield, with a CDW of 4.45 mg/L. Acetification rates were also higher in the LSK-AAB system, reaching 3.33 ± 0.05 g/L d as opposed to 2.45 ± 0.05 g/L d for LS-AAB and 1.13 ± 0.05 g/L d for planktonic cells. Additionally, BC biofilm formation during the second operational cycle was more pronounced in the LSK-AAB system (37.0 ± 3.0 mg/L, as opposed to 25.0 ± 2.0 mg/L in LS-AAB). Conclusions: This study demonstrates that LS significantly improves the efficiency of the acetification process, particularly when enhanced with κ-carrageenan. The increased biomass yield, accelerated acetification, and enhanced BC biofilm formation highlight the potential of the LS-AAB system, and especially the LSK-AAB variant, in sustainable and effective vinegar production. These systems offer a promising approach for small-scale, semi-continuous acetification processes that aligns with eco-friendly practices and caters to specialized market needs. Finally, this innovative method facilitates the dual production of acetic acid and bacterial cellulose, with potential applications in biotechnological fields. [ABSTRACT FROM AUTHOR]

This study focuses on the investigation and comparison of combustion characteristic parameters and combustion performance indices between fast-growing trees and agricultural residues as biomass sources. The investigation is conducted through direct combustion in an air environment using a thermogravimetric analyzer (TGA). Additionally, partial least squares regression (PLSR)-based models were developed to assess combustion performance indices via near-infrared spectroscopy (NIRS), serving as a non-destructive alternative method. The results obtained through the TGA reveal that, specifically, fast-growing trees display higher average ignition temperature (227 °C) and burnout temperature (521 °C) in comparison to agricultural residues, which exhibit the values of 218 °C and 515 °C, respectively. Therefore, fast-growing trees are comparatively difficult to ignite, but sustain combustion over extended periods, yielding higher temperatures. However, despite fast-growing trees having a high ignition index (Di) and burnout index (Df), the comprehensive combustion performance (Si) and flammability index (Ci) of agricultural residue are higher, indicating the latter possess enhanced thermal and combustion reactivity, coupled with improved combustion stability. Five distinct PLSR-based models were developed using 115 biomass samples for both chip and ground forms, spanning the wavenumber range of 3595–12,489 cm−1. The optimal model was selected by evaluating the coefficients of determination in the prediction set (R2P), root mean square error of prediction (RMSEP), and RPD values. The results suggest that the proposed model for Df, obtained through GA-PLSR using the first derivative (D1), and Si, achieved through full-PLSR with MSC, both in ground biomass, is usable for most applications, including research. The model yielded, respectively, an R2P, RMSEP, and RPD, which are 0.8426, 0.4968 wt.% min⁻4, and 2.5; and 0.8808, 0.1566 wt.%2 min⁻2 °C⁻3, and 3.1. The remaining models (Di in chip and ground, Df, and Si in chip, and Ci in chip and ground biomass) are primarily applicable only for rough screening purposes. However, including more representative samples and exploring a more suitable machine learning algorithm are essential for updating the model to achieve a better nondestructive assessment of biomass combustion behavior. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Accurate non-destructive assessment of biomass energy properties is essential for optimizing its use as an alternative fuel. In this study, 200 biomass samples were used to determine higher heating value (HHV) and 120 biomass samples for analyzing ultimate analysis parameters using near-infrared spectroscopy within the full wavenumber range of 12489.48 - 3594.87 cm-1. The samples were grounded, and five different types of partial least squares regression (PLSR) models were developed using traditional preprocessing, multi-preprocessing (MP) with 5 range, MP with 3 range, genetic algorithm, and successive projection algorithm. Limit of detection (LOD) and quantification (LOQ) were calculated using the best-performing model among five different PLSR models for HHV in kJ/kg, as well as the weight percentage (wt.%) of carbon (C), oxygen (O), hydrogen (H), and nitrogen (N). The LOD and LOQ for HHV were calculated as 622.42 kJ/kg and 1886.13 kJ/kg, respectively. Additionally, LOD and LOQ for ultimate analysis parameters, including C, O, H, and N were calculated as: 3.24 weight percentage (wt.%) and 9.81 wt.% for C, 2.04 wt.% and 6.18 wt.% for O, 0.35 wt.% and 1.05 wt.% for H, and 0.22 wt.% and 0.68 wt.% for N. The LOD and LOQ values for HHV, C, O, and H were lower than the minimum reference values used for model development, demonstrating the models' high sensitivity and potential to reliably detect and precisely quantify these parameters. However, the LOD and LOQ values exceeded the minimum reference value used during model development for the N, indicating that the selected models have certain limitations in assessing the N content in biomass. The sample range should be expanded for wt.% of N to enhance the model's performance, surpassing the LOD and LOQ values. This will improve the overall sensitivity of the model for reliable detection and quantification of N content in biomass samples. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The near infrared (NIR) spectroscopy both on-line and off-line scanning was applied on mango fruits (Mangifera indica CV. ‘Nam dok mai- si Thong’) for the overall precision test. The reference parameter was total soluble solids content (Brix value). The results showed that the off-line scanning had a higher accuracy than on-line scanning. The scanning repeatability of the off-line and on-line systems were 0.00199 and 0.00993, respectively. The scanning reproducibility of the off-line and online systems were 0.00279 and 0.00513, respectively. The reference of measurement repeatability was 0.2. The maximum coefficient of determination (R2max) of the reference measurement was 0.894.

Near infrared (NIR) spectroscopy is a rapid technique for nondestructive testing. Mango is popular fruit in Thailand. Therefore, The main aim of this paper is to report an overall precision of the NIR spectroscopy instruments and reference methods for determination at the beginning of the experiment for prediction models development to be in the mango applied processing factory.. Results showed that the repeatability of FT-NIR spectrometer and UV-VIS-NIR spectrometer were 0.00191 and 0.00529, respectively. The reproducibility of FT-NIR spectrometer and UV-VIS-NIR spectrometer were 0.00323 and 0.03561, respectively. Repeatability of reference test of TSS and pH were 0.1657 and 0.0827. Therefore, the R2max of TSS and pH were 0.9825 and 0.9504 which indicates that it is possible to develop NIR model for prediction of total soluble solids and pH.

The higher heating value (HHV) plays a significant role in determining of the energy potential of biomass. The method to receive this value using a bomb calorimeter takes a long time approximately of 15 minutes. The implementation of near infrared spectroscopy (NIR) technique by generating a prediction model can be replaced the former method in order to reduce time into 2 minutes. Therefore, the precision of both techniques have to be concerned in order to get the reliable values. The repeatability and reproducibility processes were carried out to evaluate the precision of both methods. It was found that the percentage difference of standard deviation (SD) from the mean of repeatability for the NIR was less than 5 % and for reproducibility was slightly over 5 % (but not over than 10%). The SD of repeatability and reproducibility of the bomb calorimeter were 54.6 J/g (0.3 % difference of average HHV value) and 81.3 J/g (0.5 % difference of average HHV value) respectively. The average HHV from the bomb calorimeter was 18,166.77±236.29 J/g. Both techniques showed very low percentage difference of the SD from the mean that revealed very high reliability and precision of measurements.