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Author: search.filters.author.Khurnpoon, LampanSubject: search.filters.subject.NIR spectroscopy

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    Comparing Machine Learning and PLSDA Algorithms for Durian Pulp Classification Using Inline NIR Spectra
    (2023-06-01)
    Pokhrel, Dharma Raj
    ;
    Sirisomboon, Panmanas
    ;
    Khurnpoon, Lampan
    ;
    Posom, Jetsada
    ;
    Saechua, Wanphut
    The aim of this study was to evaluate and compare the performance of multivariate classification algorithms, specifically Partial Least Squares Discriminant Analysis (PLS-DA) and machine learning algorithms, in the classification of Monthong durian pulp based on its dry matter content (DMC) and soluble solid content (SSC), using the inline acquisition of near-infrared (NIR) spectra. A total of 415 durian pulp samples were collected and analyzed. Raw spectra were preprocessed using five different combinations of spectral preprocessing techniques: Moving Average with Standard Normal Variate (MA+SNV), Savitzky–Golay Smoothing with Standard Normal Variate (SG+SNV), Mean Normalization (SG+MN), Baseline Correction (SG+BC), and Multiplicative Scatter Correction (SG+MSC). The results revealed that the SG+SNV preprocessing technique produced the best performance with both the PLS-DA and machine learning algorithms. The optimized wide neural network algorithm of machine learning achieved the highest overall classification accuracy of 85.3%, outperforming the PLS-DA model, with overall classification accuracy of 81.4%. Additionally, evaluation metrics such as recall, precision, specificity, F1-score, AUC ROC, and kappa were calculated and compared between the two models. The findings of this study demonstrate the potential of machine learning algorithms to provide similar or better performance compared to PLS-DA in classifying Monthong durian pulp based on DMC and SSC using NIR spectroscopy, and they can be applied in the quality control and management of durian pulp production and storage.
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    Rapid measurement of classification levels of primary macronutrients in durian (Durio zibethinus Murray CV. Mon Thong) leaves using FT-NIR spectrometer and comparing the effect of imbalanced and balanced data for modelling
    (2022-11-15)
    Phanomsophon, Thitima
    ;
    Jaisue, Natthapon
    ;
    Worphet, Akarawhat
    ;
    Tawinteung, Nukoon
    ;
    Shrestha, Bijendra
    For durian growth to produce high-quality fruit, plants should receive sufficient nutrients. Currently, farmers apply various fertilisers to produce a large quantity and quality of durian fruit, irrespective of the actual nutrients that the plant requires. Accordingly, the production cost is high and non-renewable resources. Therefore, this study focused on rapid classification primary macronutrient levels in durian (Durio zibethinus Murray CV. Mon Thong) leaves using Fourier transform near-infrared (FT-NIR) spectroscopy and investigated the effect of imbalanced data on efficient classification models. Contents of N, P, and K in durian leaves were measured via NIR with the wavelength range of 800–2,500 nm. Classification models were developed using partial least squares, k-nearest neighbour, and artificial neural networks (ANNs) with imbalanced and balanced data. The imbalanced data were balanced using a synthetic minority oversampling technique (SMOTE). In this study, the model regarding the fresh leaf sample performed better than that for the dried ground leaf sample. Moreover, the ANN was the best algorithm, exhibiting validation accuracies of classified levels corresponding to N = 0.99 and P = 0.97 when the data were analysed with SMOTE and K = 1.00 from the original balanced data. The imbalanced data affected biased classification when the models could increase the classification accuracy by applying balanced data for modelling.