Real-time near-infrared spectroscopic inspection system for adulterated sesame oil

Kang, Sun Moon; Lee, Kang-jin; Son, Jae-Ryong; Kim, Moon S.

doi:10.1117/12.850163

Cited by 1 publication

(1 citation statement)

References 18 publications

(13 reference statements)

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“…These shortcomings underscore the urgency for a rapid, non-destructive, and precise method for detecting germination rates, aimed at minimizing costs while enhancing detection speed and accuracy. Near-infrared spectroscopy (NIR) technology, capable of providing characteristic information about substances, enables efficient and economical sample analysis without causing sample destruction, thereby presenting itself as a viable tool for detecting germination rates in seed batches [8,9].…”

Section: Introductionmentioning

confidence: 99%

Near-Infrared Spectral Analysis for Assessing Germination Rate of Rapeseed Seeds: An Applied Sciences Approach

Zhang,

Lv,

Cui

et al. 2023

Applied Sciences

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Brassica rapa, commonly known as the rapeseed plant, is globally recognized for its nutrient-rich composition and oil-packed seeds, earning its distinction as a substantial oil-seed crop. The seed quality, particularly the germination rate, is instrumental in guaranteeing a high-yield rapeseed crop. Given this, the accurate, quantitative determination and selection of germination rates in seed batches prior to sowing is of paramount importance. However, conventional germination tests, employed to determine the average germination rate of seed batches, are marred by substantial time and cost inefficiencies. This study proposes the use of near-infrared spectral analysis as a proficient, non-invasive approach for assessing germination rates in rapeseed seed batches. The research involved artificial aging of seeds procured from a variety of rapeseed strains, resulting in 228 batches with a broad germination rate spectrum of 15.73% to 99.13%. We recorded near-infrared diffuse reflectance spectra and applied a range of strategies for spectral data preprocessing and feature variable selection. Furthermore, we leveraged support vector regression (SVR) modeling to augment the detection methodology. SVR training and detection were conducted using MATLAB, with selected feature wavelengths undergoing rigorous scrutiny and discussion. The results indicated that employing Savitzky–Golay convolution smoothing for spectral preprocessing, along with Synergy interval Partial Least Squares (SiPLS) in conjunction with Random Frog (RF) for the selection of 50 feature wavelength points, yielded optimal germination rate prediction performance within the SVR model. The coefficients of determination (R2c) for the training set and (R2p) for the testing set were observed to be 0.8559 and 0.8386, respectively, while the Root Mean Square Errors of Calibration (RMSEC) and Prediction (RMSEP) were calculated to be 13.76% and 17.04%. The mechanism of detecting seed vigor through near-infrared spectroscopy was analyzed based on joint variable screening and sensitive variable traceability. Consequently, the SG–SiPLS–RF–SVR model demonstrates its effectiveness in predicting the average germination rate of seed batches, offering a rapid, non-invasive detection method that can be universally applied to various rapeseed strains, thus significantly improving seed production efficiency.

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Section: Introductionmentioning

confidence: 99%