Analysis of the Pomelo Peel Essential Oils at Different Storage Durations Using a Visible and Near-Infrared Spectroscopic on Intact Fruit.

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, ß-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, ß-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.

Development of automatic tuning for combined preprocessing and hyperparameters of machine learning and its application to NIR spectral data of coconut milk adulteration.

This study proposed a novel approach to automatically select the preprocessing methods and hyperparameters of machine learning (ML) algorithms based on their best performance in cross-validation for near-infrared (NIR) spectroscopy data. The proposed method simultaneously incorporates single or multiple-preprocessing steps and tunes hyperparameters to determine the best model performance for FT-NIR and Micro-NIR spectral data of coconut milk adulteration with distilled water and mature coconut water in the range of 0%-50%. Computational experiments were conducted using nine single preprocessing types, three types of ML classifier (linear discriminant analysis (LDA), k-nearest neighbour (KNN), multilayer perceptron (MLP)) and three types of ML regressor (partial least squares (PLS), KNN, MLP). The proposed performance strategy effectively addressed and produced satisfactory outcomes for classification and regression challenges in coconut milk adulteration. Finally, the results demonstrated that the proposed approach can more accurately determine the best model, particularly for NIR spectroscopy of coconut milk adulteration.

Geographical origin identification of Khao Dawk Mali 105 rice using combination of FT-NIR spectroscopy and machine learning algorithms.

The mislabelled Khao Dawk Mali 105 rice coming from other geographical region outside the Thung Kula Rong Hai region is extremely profitable and difficult to detect; to prevent retail fraud (that adversely affects both the food industry and consumers), it is vital to identify geographical origin. Near infrared spectroscopy can be used to detect the specific content of organic moieties in agricultural and food products. The present study implemented the combinatorial method of FT-NIR spectroscopy with chemometrics to identify geographical origin of Khao Dawk Mali 105 rice. Rice samples were collected from 2 different region including the north and northeast of Thailand. NIR spectra data were collected in range of 12,500 - 4,000 cm-1 (800-2,500 nm). Five machine learning algorithms including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), C-support vector classification (C-SVC), backpropagation neural networks (BPNN), hybrid principal component analysis-neural network (PC-NN) and K-nearest neighbors (KNN) were employed to classify NIR data of rice samples with full wavelength and selected wavelength by Extremely Randomized Trees (Extra trees) algorithm. Based on the findings, geographical origin of rice could be specified quickly, cheaply, and reliably using combination of NIRS and machine learning. All models creating by full wavelength and selected wavelength exhibited accuracy between 65 and 100 % for identifying geographical region of rice. It was proven that NIR spectroscopy may be used for the quick and non-destructive identification of geographical origin of Khao Dawk Mali 105 rice.

Exploring Deep Learning to Predict Coconut Milk Adulteration Using FT-NIR and Micro-NIR Spectroscopy.

Accurately identifying adulterants in agriculture and food products is associated with preventing food safety and commercial fraud activities. However, a rapid, accurate, and robust prediction model for adulteration detection is hard to achieve in practice. Therefore, this study aimed to explore deep-learning algorithms as an approach to accurately identify the level of adulterated coconut milk using two types of NIR spectrophotometer, including benchtop FT-NIR and portable Micro-NIR. Coconut milk adulteration samples came from deliberate adulteration with corn flour and tapioca starch in the 1 to 50% range. A total of four types of deep-learning algorithm architecture that were self-modified to a one-dimensional framework were developed and tested to the NIR dataset, including simple CNN, S-AlexNET, ResNET, and GoogleNET. The results confirmed the feasibility of deep-learning algorithms for predicting the degree of coconut milk adulteration by corn flour and tapioca starch using NIR spectra with reliable performance (R2 of 0.886-0.999, RMSE of 0.370-6.108%, and Bias of -0.176-1.481). Furthermore, the ratio of percent deviation (RPD) of all algorithms with all types of NIR spectrophotometers indicates an excellent capability for quantitative predictions for any application (RPD > 8.1) except for case predicting tapioca starch, using FT-NIR by ResNET (RPD < 3.0). This study demonstrated the feasibility of using deep-learning algorithms and NIR spectral data as a rapid, accurate, robust, and non-destructive way to evaluate coconut milk adulterants. Last but not least, Micro-NIR is more promising than FT-NIR in predicting coconut milk adulteration from solid adulterants, and it is portable for in situ measurements in the future.

Nondestructive Detection of Pesticide Residue (Chlorpyrifos) on Bok Choi (Brassica rapa subsp. Chinensis) Using a Portable NIR Spectrometer Coupled with a Machine Learning Approach.

The contamination of agricultural products, such as vegetables, by pesticide residues has received considerable attention worldwide. Pesticide residue on vegetables constitutes a potential risk to human health. In this study, we combined near infrared (NIR) spectroscopy with machine learning algorithms, including partial least-squares discrimination analysis (PLS-DA), support vector machine (SVM), artificial neural network (ANN), and principal component artificial neural network (PC-ANN), to identify pesticide residue (chlorpyrifos) on bok choi. The experimental set comprised 120 bok choi samples obtained from two small greenhouses that were cultivated separately. We performed pesticide and pesticide-free treatments with 60 samples in each group. The vegetables for pesticide treatment were fortified with 2 mL/L of chlorpyrifos 40% EC residue. We connected a commercial portable NIR spectrometer with a wavelength range of 908-1676 nm to a small single-board computer. We analyzed the pesticide residue on bok choi using UV spectrophotometry. The most accurate model correctly classified 100% of the samples used in the calibration set in terms of the content of chlorpyrifos residue on samples using SVM and PC-ANN with raw data spectra. Thus, we tested the model using an unknown dataset of 40 samples to verify the robustness of the model, which produced a satisfactory F1-score (100%). We concluded that the proposed portable NIR spectrometer coupled with machine learning approaches (PLS-DA, SVM, and PC-ANN) is appropriate for the detection of chlorpyrifos residue on bok choi.