Hyperspectral Imaging-Based Multiple Predicting Models for Functional Component Contents in Brassica juncea

Abstract

Partial least squares regression (PLSR) prediction models were developed using hyperspectral imaging for noninvasive detection of the five most representative functional components in Brassica juncea leaves: chlorophyll, carotenoid, phenolic, glucosinolate, and anthocyanin contents. The region of interest for functional component analysis was chosen by polygon selection and the extracted average spectra were used for model development. For pre-processing, 10 combinations of Savitzky–Golay filter (S. G. filter), standard normal variate (SNV), multiplicative scatter correction (MSC), 1st-order derivative (1st-Der), 2nd-order derivative (2nd-Der), and normalization were applied. Root mean square errors of calibration (RMSEP) was used to assess the performance accuracy of the constructed prediction models. The prediction model for total anthocyanins exhibited the highest prediction level (RV2 = 0.8273; RMSEP = 2.4277). Pre-processing combination of SNV and 1st-Der with spectral data resulted in high-performance prediction models for total chlorophyll, carotenoid, and glucosinolate contents. Pre-processing combination of S. G. filter and SNV gave the highest prediction rate for total phenolics. SNV inclusion in the pre-processing conditions was essential for developing high-performance accurate prediction models for functional components. By enabling visualization of the distribution of functional components on the hyperspectral images, PLSR prediction models will prove valuable in determining the harvest time.

Predicting Models for Plant Metabolites Based on PLSR, AdaBoost, XGBoost, and LightGBM Algorithms Using Hyperspectral Imaging of Brassica juncea

“…The plant growth conditions and analysis methods used for this study were the same as those described in detail in the previous study [9]. Briefly, mustard plants (B. juncea L.…”

“…As with the experimental setup in the previous study [9], the hyperspectral imaging system consisted of a hyperspectral imaging camera (MicroHSI 410 SHARK; Corning Inc., Corning, NY, USA) and eight 15 W halogen lamps. A total of 112 hyperspectral images were acquired, with 1408 spatial pixels and 150 spectral bands in the range of 400-1000 nm.…”

“…A total of 112 hyperspectral images were acquired, with 1408 spatial pixels and 150 spectral bands in the range of 400-1000 nm. After the hyperspectral imaging data were obtained, the leaves were freezedried for 4 days and powdered for component analysis [9,15]. The powder obtained using pulverization after freeze-drying was subdivided into 3 repetitions of 20 mg each and used for the analysis of 5 functional components.…”

“…Partial least squares regression (PLSR) is a widely used method to analyze large amounts of hyperspectral data and predict functional components in plants, such as chlorophyll and carotenoids in spinach [7] or total polyphenols in cocoa beans [8]. In our previous study, we aimed to develop a predictive model for the functional components of mustard plants using a PLSR prediction model based on hyperspectral images and preprocessing techniques [9]. In that study, we found that a preprocessing combination of SNV transformation and 1st-Der with spectral data resulted in high-performance prediction models for the total chlorophyll, carotenoid, and glucosinolate contents, while a preprocessing combination of the S.G. filter and SNV transformation gave the highest prediction rate for the total phenolics.…”

Non-destructive prediction of rosmarinic acid content in basil plants using a portable hyperspectral imaging system and ensemble learning algorithms

“…In HSI analysis, image segmentation is employed to identify the region of interest. This can be achieved using various methods, such as histogram-based thresholding [23], rectangular or polygon region selections [13,24], and classi cation algorithms [25]. Spectral data pre-processing methods are employed to improve the quality of spectral data by eliminating noise and correcting scattering.…”