Variation analysis in spectral indices of volatile chlorpyrifos and non-volatile imidacloprid in jujube (Ziziphus jujuba Mill.) using near-infrared hyperspectral imaging (NIR-HSI) and gas chromatograph-mass spectrometry (GC–MS)

Wen-Hao Su, Da-Wen Sun, Jian-Guo He, Ling-Biao Zhang

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Abstract

Two pesticides in terms of chlorpyrifos and imidacloprid contaminating edible jujube fruits were determined using hyperspectral imaging (900–1700 nm) and gas chromatograph-mass spectrometry (GC–MS). Hyperspectral images of jujube samples contaminated by pesticides at different concentrations were collected. Their spectral data extracted in reflectance (RS), absorbance (AS), exponent (ES) and Kubelka–Munck (K-MS), were respectively used to develop partial least squares discriminant analysis (PLSDA) and locally weighted partial least square regression (LWPLSR) models. Based on these spectral parameters, corresponding models defined as AS-PLSDA and ES-PLSDA acquired optimal results, with correlation coefficients of cross-validation (RCV) of more than 0.900 for recognition of chlorpyrifos concentrations and RCV of over 0.713 for identification of concentrations of the imidacloprid. The ES-LWPLSR model obtained the best RCV of 0.864 for quantitative determination of chlorpyrifos residuals, and the best RCV of 0.885 for determination of imidacloprid residuals. The feature wavelengths were selected based on the automatic weighted least squares and gap segment derivative (AWLS-GSD) coupled with regression coefficient (RC) method. The better performance was obtained by the resulting simplified ES-AWLS-GSD-RC-LWPLSR model established using only eight characteristic wavelengths, with RCV of 0.757, RMSECV of 3.75 × 10−3 for chlorpyrifos residuals, and RCV of 0.898, RMSECV of 0.311 × 10−3 for imidacloprid residuals. To summarize, hyperspectral imaging technology shows a great potential to predict pesticide residuals of jujube fruit.
Original languageUndefined/Unknown
JournalComputers and Electronics in Agriculture
Volume139
DOIs
StatePublished - 2017

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