Monitoring chlorophyll changes during Tencha processing using portable near-infrared spectroscopy

Nondestructive detection of lead content in oilseed rape leaves under silicon action using hyperspectral image

This study explored the feasibility of employing hyperspectral imaging (HSI) technology to quantitatively assess the effect of silicon (Si) on lead (Pb) content in oilseed rape leaves. Aiming at the defects of hyperspectral data with high dimension and redundant information, this paper proposed two improved feature wavelength extraction algorithms, repetitive interval combination optimization (RICO) and interval combination optimization (ICO) combined with stepwise regression (ICO-SR). The entire oilseed rape leaves were taken as the region of interest (ROI) to extract the visible near-infrared hyperspectral data within the 400.89-1002.19 nm range. In data processing, Savitzky-Golay (SG) smoothing, detrending (DT), and multiple scatter correction (MSC) were utilized for spectral data preprocessing, while recursive feature elimination (RFE), iteratively variable subset optimization (IVSO), ICO, and the two enhanced algorithms were employed to identify characteristic wavelengths. Subsequently, based on the spectral data of preprocessing and feature extraction, partial least squares regression (PLSR) and support vector regression (SVR) methods were used to construct various Pb content prediction models in oilseed rape leaves, with a comparison and analysis of each model performance. The results indicated that the two improved algorithms were more efficient in extracting representative spectral information than conventional methods, and the performance of SVR models was better than PLSR models. Finally, to further improve the prediction accuracy and robustness of the SVR models, the whale optimization algorithm (WOA) was introduced to optimize their parameters. The findings demonstrated that the MSC-RICO-WOA-SVR model achieved the best comprehensive performance, with Rp2 of 0.9436, RMSEP of 0.0501 mg/kg, and RPD of 3.4651. The results further confirmed the great potential of HSI combined with feature extraction algorithms to evaluate the effectiveness of Si in alleviating Pb stress in oilseed rape and provided a theoretical basis for determining the appropriate amount of Si application to alleviate Pb pollution in oilseed rape.

Calcium silicate hydrate complex konjac glucomannan-based hydrogel selectively adsorbed phosphate in low alkalinity solution

The selective recovery of phosphate from wastewater can manage nutrients and realize the recycling of phosphorus resources. In this study, a novel konjac glucomannan/pectin/calcium silicate composite hydrogel (KP-CSH) was developed for efficient recovery of phosphate in aqueous solution. The amount of alkali released after the reaction of KP-CSH in a neutral solution was small (the pH of the solution after the reaction was < 9). In a wide initial pH range (3-10), the adsorption capacity of KP-CSH in 50 mg-P/L phosphate solution reached 39∼45 mg-P/g. Besides, even if the pH of the solution after the reaction was less than 8, it could still well adsorb phosphate. The kinetic and isothermal adsorption experiments indicated that the adsorption process of phosphate by KP-CSH was chemical adsorption, and the maximum adsorption capacity was 61.2 mg-P/g. KP-CSH preferentially adsorbed phosphate even in the presence of high concentrations of competitive ions. In the actual biogas slurry, KP-CSH also exhibited the strongest selectivity/affinity for phosphate, and its distribution coefficient (Kd) was significantly higher than that of other co-existing anions and cations. The adsorption mechanism analysis indicated that Ca was the main adsorption site of KP-CSH, and that the adsorption process of target pollutants mainly involved ligand exchange and the intra-sphere complexation. Further plant seed germination and seedling growth experiments suggested that KP-CSH after phosphate recovery did not exert a negative effect on the growth of plant seedlings, and increased the chlorophyll content of seedling leaves. These results demonstrate that KP-CSH is a potential adsorbent for efficient phosphate recovery, which can be used as a slow-release phosphate fertilizer after recovering phosphate.

Integrative phenotyping analyses reveal the relevance of the phyB-PIF4 pathway in Arabidopsis thaliana reproductive organs at high ambient temperature

BACKGROUND

The increasing ambient temperature significantly impacts plant growth, development, and reproduction. Uncovering the temperature-regulating mechanisms in plants is of high importance, for increasing our fundamental understanding of plant thermomorphogenesis, for its potential in applied science, and for aiding plant breeders in improving plant thermoresilience. Thermomorphogenesis, the developmental response to warm temperatures, has been primarily studied in seedlings and in the regulation of flowering time. PHYTOCHROME B and PHYTOCHROME-INTERACTING FACTORs (PIFs), particularly PIF4, are key components of this response. However, the thermoresponse of other adult vegetative tissues and reproductive structures has not been systematically evaluated, especially concerning the involvement of phyB and PIFs.

RESULTS

We screened the temperature responses of the wild type and several phyB-PIF4 pathway Arabidopsis mutant lines in combined and integrative phenotyping platforms for root growth in soil, shoot, inflorescence, and seed. Our findings demonstrate that phyB-PIF4 is generally involved in the relay of temperature signals throughout plant development, including the reproductive stage. Furthermore, we identified correlative responses to high ambient temperature between shoot and root tissues. This integrative and automated phenotyping was complemented by monitoring the changes in transcript levels in reproductive organs. Transcriptomic profiling of the pistils from plants grown under high ambient temperature identified key elements that may provide insight into the molecular mechanisms behind temperature-induced reduced fertilization rate. These include a downregulation of auxin metabolism, upregulation of genes involved auxin signalling, miRNA156 and miRNA160 pathways, and pollen tube attractants.

CONCLUSIONS

Our findings demonstrate that phyB-PIF4 involvement in the interpretation of temperature signals is pervasive throughout plant development, including processes directly linked to reproduction.

Predictive models for sensory score and physicochemical composition of Yuezhou Longjing tea using near-infrared spectroscopy and data fusion

In this study, NIR quantitative prediction model was established for sensory score and physicochemical components of different varieties and quality grades of Yuezhou Longjing tea. Firstly, L, a, b color factors and diffuse reflection spectral data are collected for each sample. Subsequently, the original spectrum is preprocessed. Three techniques for selecting variables, CARS, BOSS, and SPA, were utilized to extract optimal feature bands. Finally, the spectral data extracted from feature bands were fused with L, a and b color factors to build SVR and PLSR prediction models. enabling the rapid non-destructive discrimination of different varieties and grades of Yuezhou Longjing tea. The outcomes demonstrated that BOSS was the best variable selection technique for sensory score and the distinctive caffeine wavelengths, CARS, however, was the best variable selection technique for catechins distinctive wavelengths. Additionally, the middle-level data fusion-based non-linear prediction models greatly outperformed the linear prediction models. For the prediction models of sensory score, catechins, and caffeine, the relative percent deviation (RPD) values were 2.8, 1.6, and 2.6, respectively, suggesting the good predictive ability of the models. In conclusion, evaluating the quality of the five Yuezhou Longjing tea varieties using near-infrared spectroscopy and data fusion have proved as feasible.

Application, challenges and future prospects of recent nondestructive techniques based on the electromagnetic spectrum in food quality and safety

Safety and quality aspects of food products have always been critical issues for the food production and processing industries. Since conventional quality measurements are laborious, time-consuming, and expensive, it is vital to develop new, fast, non-invasive, cost-effective, and direct techniques to eliminate those challenges. Recently, non-destructive techniques have been applied in the food sector to improve the quality and safety of foodstuffs. The aim of this review is an effort to list non-destructive techniques (X-ray, computer tomography, ultraviolet-visible spectroscopy, hyperspectral imaging, infrared, Raman, terahertz, nuclear magnetic resonance, magnetic resonance imaging, and ultrasound imaging) based on the electromagnetic spectrum and discuss their principle and application in the food sector. This review provides an in-depth assessment of the different non-destructive techniques used for the quality and safety analysis of foodstuffs. We also discussed comprehensively about advantages, disadvantages, challenges, and opportunities for the application of each technique and recommended some solutions and developments for future trends.

Dynamic Nondestructive Detection Models of Apple Quality in Critical Harvest Period Based on Near-Infrared Spectroscopy and Intelligent Algorithms

Apples are usually bagged during the growing process, which can effectively improve the quality. Establishing an in situ nondestructive testing model for in-tree apples is very important for fruit companies in selecting raw apple materials for valuation. Low-maturity apples and high-maturity apples were acquired separately by a handheld tester for the internal quality assessment of apples developed by our group, and the effects of the two maturity levels on the soluble solids content (SSC) detection of apples were compared. Four feature selection algorithms, like ant colony optimization (ACO), were used to reduce the spectral complexity and improve the apple SSC detection accuracy. The comparison showed that the diffuse reflectance spectra of high-maturity apples better reflected the internal SSC information of the apples. The diffuse reflectance spectra of the high-maturity apples combined with the ACO algorithm achieved the best results for SSC prediction, with a prediction correlation coefficient (Rp) of 0.88, a root mean square error of prediction (RMSEP) of 0.5678 °Brix, and a residual prediction deviation (RPD) value of 2.466. Additionally, the fruit maturity was predicted using PLS-LDA based on color data, achieveing accuracies of 99.03% and 99.35% for low- and high-maturity fruits, respectively. These results suggest that in-tree apple in situ detection has great potential to enable improved robustness and accuracy in modeling apple quality.

Application of colorimetric sensor array coupled with chemometric methods for monitoring the freshness of snakehead fillets

Total volatile basic nitrogen content (TVB-N) is an important index of freshness for snakehead. This paper attempted the feasibility of determining TVB-N content level in snakehead fillets by a colorimetric sensor array (CSA) composed of twelve porphyrin materials and eight pH indicators. The nine feature variables in RGB, HSV and CIE L*a*b* color spaces were obtained by differentiating the images of the CSA before and after exposure to the headspace-gas of the samples. Competitive adaptive reweighted sampling combined with partial least squares regression (CARS-PLS) was used to build the relationship between the TVB-N content and the feature variables of CSA, and to select meaningful color-sensitive materials. The results showed that CARS-PLS had a correlation coefficient of 0.9325 in the prediction set and selected 13 informative color-sensitive materials. This study demonstrated that the CSA with CARS-PLS algorithm could be used successfully to quantify and monitor the TVB-N in snakehead fillets.

Comprehensive effects of thiamethoxam from contaminated soil on lettuce growth and metabolism

The second-generation neonicotinoid thiamethoxam, is prevalent in soils because of its extensive application and persistence. However, the comprehensive effects of thiamethoxam residue in soils on cultivated plants are still poorly understood. This study examined variations of growth state, physiological parameters, antioxidant activity, and metabolites in lettuce after thiamethoxam exposure; the removal effects of different washing procedures were also investigated. The results indicated that thiamethoxam in soils significantly increased the fresh weight, seedling height and chlorophyll content in lettuce, and also altered its lipid, carbohydrate, nucleotide and amino acids composition based on untargeted metabolomics. KEGG pathway analysis uncovered a disruption of lipid pathways in lettuce exposed to both low and high concentrations of thiamethoxam treatments. In addition, the terminal residues of thiamethoxam in lettuce were below the corresponding maximum residue limits stipulated for China. The thiamethoxam removal rates achieved by common washing procedures in lettuce ranged from 26.9% to 42.6%. This study thus promotes the understanding of the potential food safety risk caused by residual thiamethoxam in soils.

Colorimetric sensor array combined with chemometric methods for the assessment of aroma produced during the drying of tencha

The aroma produced during drying is an important indicator of tencha and needs to be monitored. This study constructed an olfactory visualization system for assessing tencha aroma using colorimetric sensor array (CSA) combined with chemometric methods. The 16 chemically responsive dyes were selected to obtain aroma information of tencha samples and extracted image data of aroma information by machine vision algorithms. Subsequently, k-nearest neighbor, support vector machine, classification and regression tree, and random forest (RF), four qualitative models were applied to build the mathematical models. The RF model with nine principal components was preferred, with recognition rate of 100.00% and 91.07% for the training and prediction sets, respectively. The experimental results showed that CSA combined with the RF model can be effectively applied to assess tencha aroma. This study provided a scientific and novel method to maintain the stability of tencha quality in the production process.

Visible and near-infrared spectroscopic determination of sugarcane chlorophyll content using a modified wavelength selection method for multivariate calibration

Spectroscopy in the visible and near-infrared region (Vis-NIR) region has proven to be an effective technique for quantifying the chlorophyll contents of plants, which serves as an important indicator of their photosynthetic rate and health status. However, the Vis-NIR spectroscopy analysis confronts a significant challenge concerning the existence of spectral variations and interferences induced by diverse factors. Hence, the selection of characteristic wavelengths plays a crucial role in Vis-NIR spectroscopy analysis. In this study, a novel wavelength selection approach known as the modified regression coefficient (MRC) selection method was introduced to enhance the diagnostic accuracy of chlorophyll content in sugarcane leaves. Experimental data comprising spectral reflectance measurements (220-1400 nm) were collected from sugarcane leaf samples at different growth stages, including seedling, tillering, and jointing, and the corresponding chlorophyll contents were measured. The proposed MRC method was employed to select optimal wavelengths for analysis, and subsequent partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed to establish the relationship between the selected wavelengths and the measured chlorophyll contents. In comparison to full-spectrum modelling and other commonly employed wavelength selection techniques, the proposed simplified MRC-GPR model, utilizing a subset of 291 selected wavelengths, demonstrated superior performance. The MRC-GPR model achieved higher coefficient of determination of 0.9665 and 0.8659, and lower root mean squared error of 1.7624 and 3.2029, for calibration set and prediction set, respectively. Results showed that the GPR model, a nonlinear regression approach, outperformed the PLSR model.

Design and Experiment of a Portable Near-Infrared Spectroscopy Device for Convenient Prediction of Leaf Chlorophyll Content

This study designs a spectrum data collection device and system based on the Internet of Things technology, aiming to solve the tedious process of chlorophyll collection and provide a more convenient and accurate method for predicting chlorophyll content. The device has the advantages of integrated design, portability, ease of operation, low power consumption, low cost, and low maintenance requirements, making it suitable for outdoor spectrum data collection and analysis in fields such as agriculture, environment, and geology. The core processor of the device uses the ESP8266-12F microcontroller to collect spectrum data by communicating with the spectrum sensor. The spectrum sensor used is the AS7341 model, but its limited number of spectral acquisition channels and low resolution may limit the exploration and analysis of spectral data. To verify the performance of the device and system, this experiment collected spectral data of Hami melon leaf samples and combined it with a chlorophyll meter for related measurements and analysis. In the experiment, twelve regression algorithms were tested, including linear regression, decision tree, and support vector regression. The results showed that in the original spectral data, the ETR method had the best prediction effect at a wavelength of 515 nm. In the training set, RMSEc was 0.3429, and Rc2 was 0.9905. In the prediction set, RMSEp was 1.5670, and Rp2 was 0.8035. In addition, eight preprocessing methods were used to denoise the original data, but the improvement in prediction accuracy was not significant. To further improve the accuracy of data analysis, principal component analysis and isolation forest algorithm were used to detect and remove outliers in the spectral data. After removing the outliers, the RFR model performed best in predicting all wavelength combinations of denoised spectral data using PBOR. In the training set, RMSEc was 0.8721, and Rc2 was 0.9429. In the prediction set, RMSEp was 1.1810, and Rp2 was 0.8683.

Application of portable Vis-NIR spectroscopy for rapid detection of myoglobin in frozen pork

Myoglobin content is considered as a crucial index to evaluate the quality of frozen pork. In this study, a portable visible and near-infrared (Vis-NIR) spectrometer combined with chemometrics was used to detect myoglobin content in frozen pork. Metmyoglobin, deoxymyoglobin, oxymyoglobin, and total myoglobin were assessed spectrophotometrically. The raw Vis-NIR spectra of frozen pork samples were pre-processed using 1^st derivatives (FD). Afterward, Synergy Interval Partial Least Square (Si-PLS) coupled Competitive Adaptive Reweighted Sampling algorithm (Si-CARS-PLS) was applied to select characteristic variables. The Si-CARS-PLS models revealed the probability of estimating myoglobin content in frozen pork, with predictive correlation coefficients (R_p) for metmyoglobin, deoxymyoglobin, oxymyoglobin, and total myoglobin as 0.9095, 0.9004, 0.8578, and 0.9133, respectively. The findings of this study showed that Vis-NIR spectroscopy coupled with Si-CARS-PLS is a promising method and offered a way forward for determining the myoglobin content in frozen pork.

Application of visible-near infrared spectroscopy in tandem with multivariate analysis for the rapid evaluation of matcha physicochemical indicators

Consumer preference for matcha is heavily influenced by its physicochemical properties. The visible-near infrared (Vis-NIR) spectroscopy technology coupled with multivariate analysis was investigated for rapid and non-invasive evaluation of particle size and the ratio of tea polyphenols to free amino acids (P/F ratio) of matcha. The multivariate selection algorithms such as synergy interval (Si), variable combination population analysis (VCPA), competitive adaptive reweighted sampling (CARS), and interval combination population analysis (ICPA) were compared, and eventually, the variable selection strategy of ICPA and CARS hybridization was firstly proposed for selecting the characteristic wavelengths from Vis-NIR spectra to build partial least squares (PLS) models. Results indicated that the ICPA-CARS-PLS models achieved satisfactory performance for the evaluation of matcha particle size (Rp = 0.9376) and P/F ratio (Rp = 0.9283). Hence the rapid, effectual, and nondestructive online monitoring, Vis-NIR reflectance spectroscopy in tandem with chemometric models is significant for the industrial production of matcha.

Application of colorimetric sensor array combined with visible near-infrared spectroscopy for the matcha classification

Matcha tea powder is considered as an integral part of a healthy diet due to its enormous health benefits. In the current study, visible near-infrared (Vis-NIR) and colorimetric sensor array (CSA) techniques are applied to identify the matcha grades. The color-sensitive dyes reacted with their volatile compounds and the information was recorded by Vis-NIR spectroscopy, namely Vis-NIR-CSA. Specifically, three linear and three nonlinear classification models were compared, yielding the optimal identification rate by the back-propagation artificial neural network (BPANN) model with 99% and 98% in the calibration and prediction sets, respectively. The results indicated the sensor combined with the BPANN model could be applied satisfactorily in identification of different matcha grades. Additionally, the variations in volatile compounds between different matcha grades and eight characteristic volatile compounds were identified, which verified the sensor identification results. This study provided a scientific and novel method for the stability of matcha quality in production.