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Author: search.filters.author.Phetpan, KittisakSubject: search.filters.subject.Near-infrared spectroscopy

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    Application of near-infrared spectroscopy in detection of steroids adulteration in traditional thai medicines
    (2022-01-01)
    Sirisomboon, Panmanas
    ;
    Phetpan, Kittisak
    This study aimed to focus on applying near-infrared (NIR) spectroscopy to identify the adulteration of traditional Thai medicine products (TTM) with steroids. One hundred and ten samples were prepared with pure TTM and ten different steroid concentrations (0.25-5 mg steroid/g TTM). Fourier transform near-infrared (FT-NIR) spectrometer was used to scan TTM samples. The partial least squares (PLS) regression was used for the NIR spectroscopic model development to predict the level of steroid adulteration in TTM. For classification analysis, the principal component analysis (PCA) was used to discriminate 11 groups of raw TTM spectra (220 spectra). The developed PLS model accompanied by 3 latent variables (LVs) could predict the steroid content in TTM accurately with the coefficient of determination of prediction (r<sup>2</sup>) of 98.20%, root mean square error of prediction (RMSEP) of 0.22 mg steroid/g TTM, and residual prediction deviation (RPD) of 7.46. Furthermore, the PCA approach was possible to discriminate among the groups of TTM. The study showed NIR spectroscopy's capability to be used as a powerful technique to evaluate the steroid adulterated in TTM. This report is useful for food and drug association, patients, pharmaceuticals, and medical sectors.
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    Evaluation of physiological properties and texture traits of durian pulp using near-infrared spectra of the pulp and intact fruit
    (2021-04-01)
    Onsawai, Phalanon
    ;
    Phetpan, Kittisak
    ;
    Khurnpoon, Lampan
    ;
    Sirisomboon, Panmanas
    This study aimed to investigate the feasibility of non-destructively predicting physiological properties (color, dry matter, and soluble solids) and texture properties (initial firmness, average firmness, rupture force, rupture distance, toughness, average penetrating force, and penetrating energy) of ‘Monthong’ durian using Fourier transform near-infrared spectroscopy of the pulp of the largest locule, the intact fruit at the largest locule, and the stylar end of the intact fruit. Based on partial least squares regression modeling, the internal quality evaluation of durian obtained by scanning the pulp could provide rough screening capability, with coefficient of determination of validation (r<sup>2</sup>), root mean square error of prediction (RMSEP), and the ratio of standard error of validation to standard deviation (RPD) values for the dry matter content, average penetrating force, and rupture force of 0.89, 3.60%, and 3.27; 0.73, 5.53 N, and 1.95; and 0.74, 6.15 N, and 1.96, respectively. Only the dry matter content of the pulp could be reasonably predicted based on scanning the intact durian fruit at the largest locule, with r<sup>2</sup>, RMSEP, and RPD values of 0.79, 5.23%, and 2.18, respectively. This finding could be applied at the first stage of trade between durian agriculturalists and exporters to prevent the exportation of immature durian and would be helpful for the industries producing frozen durian pulp and intact durian fruit for export.
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    Development of the partial least-squares model to determine the soluble solids content of sugarcane billets on an elevator conveyor
    (2021-01-01)
    Udompetaikul, Vasu
    ;
    Phetpan, Kittisak
    ;
    Sirisomboon, Panmanas
    This study aimed to determine the optimum multivariate model for monitoring the soluble solids content (SSC) of sugarcane billets being transferred on a conveyor. The study covered two main issues: the exploration of an appropriate spectral range (450–900 nm versus 700–900 nm) and the assessment of the influence of different levels of cane billets on an elevator via modelling to predict the SSC values. Partial least squares regression (PLSR) was used for model development. Modelling using the range of 450–900 nm employed 4 latent variables (LVs) and showed the coefficient of determination (R<sup>2</sup>) and root mean squares error of prediction (RMSEP) of 0.83 and 0.29 °Brix, respectively. This caused the model established using the range of 700–900 nm, employed 3 LVs and provided the R<sup>2</sup> and RMSEP values of 0.81 and 0.31 °Brix, respectively, seems more appropriate. In case of assessing the different cane levels on the conveyor, the outcomes presented model performance of the full and half cane levels in predicting half and full cane datasets with R<sup>2</sup> and RMSEP of 0.52 and 0.55 °Brix and 0.53 and 0.48 °Brix, respectively. This showed that the different levels affected the SSC predictive accuracy of the model. The combined model was developed to cover variations of this difference and was used to predict two external sets. The predictions of ninety and thirty samples that were collected from the same and different growing seasons as the samples for the modelling presented the R<sup>2</sup>, RMSEP and RPD of 0.70, 0.42 °Brix and 1.83 and 0.56, 0.42 °Brix and 2.00, respectively.
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    An online visible and near-infrared spectroscopic technique for the real-time evaluation of the soluble solids content of sugarcane billets on an elevator conveyor
    (2018-11-01)
    Phetpan, Kittisak
    ;
    Udompetaikul, Vasu
    ;
    Sirisomboon, Panmanas
    The aim of this research study is to propose a prototype online detection system based on the visible and near-infrared spectroscopic (vis/SW-NIR) technique for the real-time evaluation of the soluble solids content (SSC) of sugarcane billets on an elevator conveyor. The system consisted of two main parts, a cane billet elevator and a spectral acquisition device. An elevator speed of 2 m/s was used for the transfer of sugarcane billets. Spectra acquisition was performed using four 50 W tungsten halogen lamps as a light source in conjunction with vis/SW-NIR spectrometer in reflectance mode. Partial least squares regression (PLSR) was subsequently used to correlate the spectra with the experimentally determined SSC values. The model performance was then assessed using an independent prediction set. The model was found to display a coefficient of determination of prediction (R<sup>2</sup>) of 0.785, a root mean square error of prediction (RMSEP) of 0.30 and a residual predictive deviation (RPD) of 2.16. The result on the prediction set confirm that the proposed system is suitable for the online SSC measurement of the sugarcane billets on an elevator conveyor.