Screening Inorganic Arsenic in Rice by Visible and Near-Infrared Spectroscopy

Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping: A Review

Heavy metal pollution is becoming a prominent stress on plants. Plants contaminated with heavy metals undergo changes in external morphology and internal structure, and heavy metals can accumulate through the food chain, threatening human health. Detecting heavy metal stress on plants quickly, accurately, and nondestructively helps to achieve precise management of plant growth status and accelerate the breeding of heavy metal-resistant plant varieties. Traditional chemical reagent-based detection methods are laborious, destructive, time-consuming, and costly. The internal and external structures of plants can be altered by heavy metal contamination, which can lead to changes in plants' absorption and reflection of light. Visible/near-infrared (V/NIR) spectroscopy can obtain plant spectral information, and hyperspectral imaging (HSI) can obtain spectral and spatial information in simple, speedy, and nondestructive ways. These 2 technologies have been the most widely used high-throughput phenotyping technologies of plants. This review summarizes the application of V/NIR spectroscopy and HSI in plant heavy metal stress phenotype analysis as well as introduces the method of combining spectroscopy with machine learning approaches for high-throughput phenotyping of plant heavy metal stress, including unstressed and stressed identification, stress types identification, stress degrees identification, and heavy metal content estimation. The vegetation indexes, full-range spectra, and feature bands identified by different plant heavy metal stress phenotyping methods are reviewed. The advantages, limitations, challenges, and prospects of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping are discussed. Further studies are needed to promote the research and application of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping.

Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis

Heavy metal monitoring in food-producing ecosystems can play an important role in human health safety. Since they are able to interfere with plants' physiochemical characteristics, which influence the optical properties of leaves, they can be measured by in-field spectroscopy. In this study, the predictive power of spectroscopic data is examined. Five treatments of heavy metal stress (Cu, Zn, Pb, Cr, and Cd) were applied to grapevine seedlings and hyperspectral data (350-2500 nm), and heavy metal contents were collected based on in-field and laboratory experiments. The partial least squares (PLS) method was used as a feature selection technique, and multiple linear regressions (MLR) and support vector machine (SVM) regression methods were applied for modelling purposes. Based on the PLS results, the wavelengths in the vicinity of 2431, 809, 489, and 616 nm; 2032, 883, 665, 564, 688, and 437 nm; 1865, 728, 692, 683, and 356 nm; 863, 2044, 415, 652, 713, and 1036 nm; and 1373, 631, 744, and 438 nm were found most sensitive for the estimation of Cu, Zn, Pb, Cr, and Cd contents in the grapevine leaves, respectively. Therefore, visible and red-edge regions were found most suitable for estimating heavy metal contents in the present study. Heavy metals played a significant role in reforming the spectral pattern of stressed grapevine compared to healthy samples, meaning that in the best structures of the SVM regression models, the concentrations of Cu, Zn, Pb, Cr, and Cd were estimated with R2 rates of 0.56, 0.85, 0.71, 0.80, and 0.86 in the testing set, respectively. The results confirm the efficiency of in-field spectroscopy in estimating heavy metals content in grapevine foliage.

Combined analysis of near-infrared spectra, colour, and physicochemical information of brown rice to develop accurate calibration models for determining amylose content

Amylose content is an important determinant of rice quality. Accurate non-destructive determination of amylose content remains a primary challenge for the rice industry. Here, we analysed the accuracy of three models for the non-destructive determination of amylose content. The models were developed by combining near-infrared spectra, colour, and physicochemical information relative to 832 brown rice samples from ten varieties produced between 2009 and 2017 in various regions of Hokkaido, Japan. Models describing low and ordinary amylose varieties were developed individually, merged, and validated using production year samples (2016-2017) different from the calibration set (2009-2015). The resulting accuracy was suitable for industrial application. With standard error of prediction = 0.70% and ratio of performance deviation = 3.56, the combination of near-infrared spectra and physicochemical information produced the most robust model, enabling more precise rice quality screening at grain elevators.

Rapid Screening of Cadmium in Rice and Identification of Geographical Origins by Spectral Method

The accuracy, repeatability and detection limits of the energy-dispersive X-ray fluorescence (XRF) spectrometer used in this study were tested to verify its suitability for rapid screening of cadmium in samples. Concentrations of cadmium in rice grain samples were tested by the XRF spectrometer. The results showed that the apparatus had good precision around the national limit value (0.2 mg/kg). Raman spectroscopy has been analyzed in the discrimination of rice grain samples from different geographical origins within China. Scanning time has been discussed in order to obtain better Raman features of rice samples. A total of 31 rice samples were analyzed. After spectral data pre-treatment, principal component analysis (PCA), K-means clustering (KMC), hierarchical clustering (HC) and support vector machine (SVM) were performed to discriminate origins of rice samples. The results showed that the geographical origins of rice could be classified using Raman spectroscopy combined with multivariate analysis.

Exploring the use of NIR reflectance spectroscopy in prediction of free L-Asparagine in solanaceae plants

Much researches of Near-infrared spectroscopy modeling methods that are utilized to analyze the trace amount components, especially indirect modeling on complex system, have gained widely attraction in recent years. Amino acids in plants are essential nutrients of maintaining growth and ensuring health. As the important participants in various biochemical reactions in plants, nondestructive detection of free amino acids will provide meaningful observation on physiological changing in different steps of plant growth. In this research, two hundred and twenty-two samples were measured to obtain the concentration of free L-Asparagine in plant by amino acid analyzer. NIR spectra were also collected for conducting chemometrics modeling. Different spectral pretreatments and variables selecting methods were employed to optimize the NIR models. Independent validation set as well as unknown samples from different years were successfully predicted by using the slope intercept correction. Results in this study demonstrated that fast analysis of free L-Asparagine can be established by NIR modeling approach.

Proximal spectral sensing to monitor phytoremediation of metal-contaminated soils

Assessment of soil contamination and its long-term monitoring are necessary to evaluate the effectiveness of phytoremediation systems. Spectral sensing-based monitoring methods promise obvious benefits compared to field-based methods: lower cost, faster data acquisition and better spatio-temporal monitoring. This paper reviews the theoretical basis whereby proximal spectral sensing of soil and vegetation could be used to monitor phytoremediation of metal-contaminated soils, and the eventual upscaling to imaging sensing. Both laboratory and field spectroscopy have been applied to sense heavy metals in soils indirectly via their intercorrelations with soil constituents, and also through metal-induced vegetation stress. In soil, most predictions are based on intercorrelations of metals with spectrally-active soil constituents viz., Fe-oxides, organic carbon, and clays. Spectral variations in metal-stressed plants is particularly associated with changes in chlorophyll, other pigments, and cell structure, all of which can be investigated by vegetation indices and red edge position shifts. Key shortcomings in obtaining satisfactory calibration for monitoring the metals in soils or metal-related plant stress include: reduced prediction accuracy compared to chemical methods, complexity of spectra, no unique spectral features associated with metal-related plant stresses, and transfer of calibrations from laboratory to field to regional scale. Nonetheless, spectral sensing promises to be a time saving, non-destructive and cost-effective option for long-term monitoring especially over large phytoremediation areas, and it is well-suited to phytoremediation networks where monitoring is an integral part.