Abstract
Moisture content is one of the factors measured to evaluate the quality of Camellia oleifera seeds. High quality <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C. oleifera</i> seeds used for trading must have a low moisture content, specifically not more than 15% on a dry basis (db). Moisture content analysis requires a prolonged laboratory investigation so that the development of fast and effective determination methods is helpful. The objective of this paper was to develop a low-cost portable NIR reflectance spectrometer collaborating with an android application for the rapid prediction of the moisture content in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C. oleifera</i> seeds. To calibrate the prediction model, an effective chemometric algorithm, based on partial least squares regression was established, and models based on wavelength selection algorithms such as backward interval partial least squares ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bi</i> PLS) and partial least squares coupled with variable importance projection (VIP-PLS) were implemented as an improved version of PLS. Both algorithms ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bi</i> PLS and VIP-PLS) improved the predictive performance and accuracy of the model. The experimental results showed that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bi</i> PLS model with the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> derivative transformation provided the best prediction for measuring the moisture content of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C. oleifera</i> seeds with a coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) value of 0.927, standard error of prediction (SEP) of 0.848%db, bias of −0.067%db, function slope of 1.005, and ratio of performance deviation (RPD) of 3.696. Finally, the device was tested according to the ISO 12099:2017(E) standard and confirmed the reliability of the device for in-field use.
Highlights
The C. oleifera tree grows in tropical and subtropical regions of Asia, especially in southwest China [1]
The results showed that the models with wavelength selection algorithms (VIP-PLS, backward interval partial least squares (biPLS)) improved predictive performance (R2, standard error of prediction (SEP), ratio of performance deviation (RPD))
The experiments indicated that the NIR spectroscopy technique had high potential for the rapid estimation of the moisture content of C. oleifera seeds across the full range of anticipated moisture contents because the sample spectrum correlated with the water absorption bands at around 960 nm and 1455 nm
Summary
The C. oleifera tree grows in tropical and subtropical regions of Asia, especially in southwest China [1]. It has been extensively cultivated at mountainous elevations of 500–1,300 m in northern Thailand. C. oleifera is an edible tree oil like olive oil, palm oil, and coconut oil. It is known as the oriental olive oil because of its composition being similar to olive oil. C. oleifera oil is excellent for healthy cooking and contains a low amount of saturated fat. With its high quality and healthy properties, The associate editor coordinating the review of this manuscript and approving it for publication was Norbert Herencsar
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