Optimizing feature selection with gradient boosting machines in PLS regression for predicting moisture and protein in multi-country corn kernels via NIR spectroscopy.

Differences in moisture and protein content impact both nutritional value and processing efficiency of corn kernels. Near-infrared (NIR) spectroscopy can be used to estimate kernel composition, but models trained on a few environments may underestimate error rates and bias. We assembled corn samples from diverse international environments and used NIR with chemometrics and partial least squares regression (PLSR) to determine moisture and protein. The potential of five feature selection methods to improve prediction accuracy was assessed by extracting sensitive wavelengths. Gradient boosting machines (GBMs), particularly CatBoost and LightGBM, were found to effectively select crucial wavelengths for moisture (1409, 1900, 1908, 1932, 1953, 2174 nm) and protein (887, 1212, 1705, 1891, 2097, 2456 nm). SHAP plots highlighted significant wavelength contributions to model prediction. These results illustrate GBMs' effectiveness in feature engineering for agricultural and food sector applications, including developing multi-country global calibration models for moisture and protein in corn kernels.

Advancing near-infrared spectroscopy: A synergistic approach through Bayesian optimization and model stacking.

Fourier transform near-infrared (FT-NIR) spectroscopy is a versatile and non-destructive analytical tool widely utilized in industries such as food, pharmaceuticals, and agriculture. While traditional FT-NIR instruments pose limitations in terms of cost and complexity, the advent of portable and affordable systems like NeoSpectra Scanners has broadened accessibility. Partial Least Squares Regression (PLSR) stands as an industry-standard method in Chemometrics for analyzing chemical compositions. This work addresses optimizing PLSR models in FT-NIR spectroscopy, focusing on enhancing accuracy and adaptability in material analysis. Unlike traditional PLSR models which often rely on grid searching a limited number of parameters, such as latent variables, the presented approach effectively expands the parameter space. A novel framework combining Bayesian search and stacking techniques is introduced to enable more customization while ensuring time and performance efficiency, along with automation in model development. Bayesian search efficiently explores hyperparameters space, enabling faster convergence to optimal model settings without exhaustive exploration. The proposed stacked model leverages learned knowledge from the top-performing PLSR models optimized through Bayesian methods, amalgamating a unified and potent body of knowledge. Bayesian-stacked models are compared with PLSR models that use grid search for a limited parameter set. Findings show a marked improvement in model performance: a 51.5% reduction in Root Mean Square Error (RMSE) for the training dataset and a 26.1% reduction for the testing dataset, alongside a 10.9% increase in the correlation coefficient square (R2) for the training dataset and a 10.4% increase for the testing dataset. Notably, Bayesian search reduces the model optimization time by approximately 90% compared with the grid search. Furthermore, when addressing instrumental variations, the models demonstrate an additional improvement, evident in the average reduction of 24.1% in the mean range of prediction. Overall, results demonstrate that the presented approach not only increases the prediction accuracy but also offers a more efficient, automated and robust solution for diverse spectroscopic applications.

Investigation of the multiple taste enhancement properties of N-succinyl-amino acids and their relationship to chemical structure using dynamic sensory techniques.

The present study aimed to explore the similarity and difference in taste enhancement properties of N-succinyl-L-phenylalanine (N-Suc-Phe), N-succinyl-L-tryptophan (N-Suc-Trp), and N-succinyl-L-tyrosine (N-Suc-Tyr) using temporal dominance of sensations (TDS), temporal check-all-that-apply (TCATA), and time-intensity (TI) techniques. Meanwhile, leading taste enhancers in the market, such as N'-[(2,4-dimethoxyphenyl)methyl]-N-(2-pyridin-2-ylethyl) oxamide (DE) was chosen to conduct a comparative analysis with the aforementioned three compounds. Findings from TDS and TCATA revealed that all compounds under investigation notably enhanced umami and saltiness while reducing bitterness in a concentration-dependent fashion (0.25-1 mg/L). Additionally, the TI results indicated that the duration of umami was extended by 50-75%, and the duration of bitterness was decreased by 20-40% upon addition of DE, N-Suc-Phe, N-Suc-Trp, and N-Suc-Tyr (1 mg/L). Among these, N-Suc-Trp was identified as the most effective in augmenting umami and mitigating bitterness, whereas N-Suc-Tyr excelled in enhancing saltiness intensity. Partial least squares regression (PLSR) pinpointed the carbon­carbon double bond as the important structure influencing the enhancement of umami and reduction of bitterness, whereas the phenolic hydroxyl group was identified as critical for enhancing saltiness. This investigation provided insights into the different characteristics of taste enhancement of N-Suc-AAs and the impact of chemical structure on such specificity.

Application of fourier transform infrared photoacoustic spectroscopy for quantification of nutrient contents and their plant availability in manure and digestate.

In this study, we assess the feasibility of using Fourier Transform Infrared Photoacoustic Spectroscopy (FTIR-PAS) to predict macro- and micro-nutrients in a diverse set of manures and digestates. Furthermore, the prediction capabilities of FTIR-PAS were assessed using a novel error tolerance-based interval method in view of the accuracy required for application in agricultural practices. Partial Least-Squares Regression (PLSR) was used to correlate the FTIR-PAS spectra with nutrient contents. The prediction results were then assessed with conventional assessment methods (root mean square error (RMSE), coefficient of determination R2, and the ratio of prediction to deviation (RPD)). The results show the potential of FTIR-PAS to be used as a rapid analysis technique, with promising prediction results (R2 > 0.91 and RPD >2.5) for all elements except for bicarbonate-extractable P, K, and NH4+-N (0.8 < R2 < 0.9 and 2 < RPD <2.5). The results for nitrogen and phosphorus were further evaluated using the proposed error tolerance-based interval method. The probability of prediction for nitrogen within the allowed limit is calculated to be 94.6 % and for phosphorus 83.8 %. The proposed error tolerance-based interval method provides a better measure to decide if the FTIR-PAS in its current state could be used to meet the required accuracy in agriculture for the quantification of nutrient content in manure and digestate.

Qualitative discrimination and quantitative prediction of microplastics in ash based on near-infrared spectroscopy.

Microplastics are recognized as a new environmental pollutant. Researchers have detected their presence in waste incineration ash. However, traditional testing methods take a very long testing period. There is a lack of research on detecting microplastics in waste incineration ash. In this paper, a portable near-infrared spectra (NIRS) spectrometer was used for qualitative discrimination and quantitative prediction of microplastics in ash. A total of 84 sets of simulated ash samples containing different types (PP, PS, PE, and PVC) and contents (2.4 wt% - 20 wt%) of microplastics were used in the model. The results show the qualitative discrimination model using support vector machines (SVM) method with multiplicative scatter correction (MSC) preprocessing could effectively identify the microplastic types in the ash with 100% detection accuracy. Furthermore, the partial least squares regression (PLSR) model was effective in quantitatively predicting the content of microplastics in ash. The Rp2 of the PP, PS, PE, and PVC models are 0.95, 0.93, 0.89, and 0.95, respectively. The RPD of the PP, PS, PE, and PVC models are 3.97, 3.96, 2.89 and 5.02, respectively. This study shows that microplastics in ash can be detected rapidly and accurately using portable near-infrared spectrometers.

[Effects of Land Use Types on Water Quality at Different Buffer Scales: Tianjin Section of the Haihe River Basin as an Example].

It is important to explore the relationship between land use types and water quality to improve the surface water environment. Based on monthly water quality monitoring data from 16 nationally controlled surface water quality monitoring stations in Tianjin and land use data in 2021, GIS spatial analysis and mathematical and statistical methods were used to study the influence of land use types on surface water quality in buffer zones at different scales. The results showed that:① the land use types in the study area were mainly construction land, farmland, and water areas, which had significant effects on river water quality. Except for water temperature (WT) and pH, the farmland, construction land, and water areas were negatively correlated with each water quality indicator; forest land and grassland were positively correlated with dissolved oxygen (DO) and total nitrogen (TN) and negatively correlated with other water quality indicators. ② The water quality indicators showed obvious spatial differences in different seasons. The pH, DO and TN concentrations were higher in the dry season, whereas the permanganate index, ammonia nitrogen (NH4+-N), and total phosphorus (TP) concentrations were higher in the rainy season. ③ The results of the RDA analysis showed that the 800 m buffer zone land use had the greatest explanatory power for water quality changes in the dry season (50.4%), whereas the 3 000 m buffer zone land use could explain the water quality changes in the rainy season to the greatest extent (49.6%); from the average explanation rate of the dry and rainy seasons, the 3 000 m buffer zone was the best impact scale (50.0%) on water quality indicators in Tianjin. ④ The partial least squares regression (PLSR) analysis showed that the most important variables affecting surface water quality changes were construction land, farmland, and water areas. The predictive ability of the PLSR model of most water quality indicators was stronger in the dry season than that in the rainy season. In the dry season, all water quality indicators, except WT and pH, were most influenced by farmland. In the rainy season, construction land had the greatest influence on WT and NH4+-N concentrations, and the most important influencing factor for the remaining water quality indicators was still farmland. This study showed that the rational planning of land use types within 3 000 m of rivers or lakes was beneficial to improving the water quality of surface water.

Aroma Characteristics of Green Huajiao in Sichuan and Chongqing Area Using Sensory Analysis Combined with GC-MS.

Green huajiao has a unique flavor and is widely used in cooking as an edible spice. In this study, the intensity of overall aroma and aroma attributes of seven green huajiao samples from the Sichuan and Chongqing regions were evaluated using a dynamic dilution olfactometer and ranking descriptive analysis (RDA) technology. The volatile compounds and major aroma components were determined by GC-MS in combination with odor activity value (OAV) analysis. The partial least squares regression (PLSR) model was further used to identify the key aromas contributing to the aroma sensory attributes. Seven green huajiao samples were categorized into three groups: (1) huajiao samples from Liangshan have a strong intensity of pungent, floral and herbal aromas and a medium-high intensity of sweet aroma, and the key contributing aroma compounds were α-pinene, sabinene, ß-pinene, myrcene, ocimene and linalool; (2) huajiao samples from Panzhihua and Hongya have a strong intensity of citrusy, lemony and minty aromas, and the key contributing aroma compound was linalool; and (3) the huajiao sample from the Chongqing region was categorized into a separate group and was characterized by a medium-high intensity of green, minty and sweet aromas, and the main aroma compounds are ocimene, citronellal and α-terpineol. These results provide useful basic data for evaluating the aroma quality and analyzing the key aroma characteristics of green huajiao in the Sichuan and Chongqing regions.

Intelligence detection of oil absorption in French fries by surface profiles.

Surface profiles are important evaluation indices for oil absorption behavior of fried foods. This research established two intelligent models of partial least-squares regression (PLSR) and back propagation artificial neural network (BP-ANN) for monitoring the oil absorption behavior of French fries based on the surface characteristics. Surface morphology and texture of French fries by rapeseed oil (RO) and high-oleic peanut oil (HOPO) at different temperatures were investigated. Results showed that oil content of samples increased with frying temperature, accounting for 37.7% and 41.4% of samples fried by RO and HOPO respectively. The increase of crust ratio, roughness and texture parameters (Fm, Nwr, fwr, Wc) and the decrease of uniformity were observed with the frying temperature. Coefficients of prediction set of PLSR and BP-ANN models were more than 0.93, which indicated that surface features combined with chemometrics were rapid and precise methods for determining the oil content of French fries.

[Rapid detection technology of chemical component content in Lycii Fructus based on hyperspectral technology].

This Fructus,study including and aimed to construct a rapid and nondestructive detection flavonoid,model betaine,for and of the content vitamin of(Vit four four quality C).index components Lycium barbarum polysaccharide,of inL ycii rawma total and C Hyperspectral data quantitative of terials modelswere powder developed Lycii using Fructus partial were squares effects collected,regression raw based LSR),on the support content vector the above components,the forest least(P regression compared,(SVR),the and effects random three regression(RFR)were algorithms.also The Four spectral predictive commonly data of the materialsand powder were were applied and of spectral quantitative for models reduction.compared.used were pre-processing screened methods feature to successive pre-process projection the raw algorithm data(SPA),noise competitive Thepre-processed for bands using adaptive reweigh ted sampling howed(CARS),the and maximal effects relevance based and raw minimal materials redundancy and(MRMR)were algorithms Following to optimize multiplicative the models.scatter The correction Based resultss(MS that prediction SPA on feature the powder prediction similar.PLSR C)denoising sproposed and integrated for model,screening the the coefficient bands,determination the effect(R_C~2)of(MSC-SPA-PLSR)coefficient was optimal.of on(R_P~2)thi of of calibration flavonoid,and and of all determination greater prediction0.83,L.barbarum inconte nt prediction of polysaccharide,total mean betaine,of Vit C were than smallest In the compared study,root with mean other prediction content squareserror models of the calibration(RMSEC)residual and deviation root squares was error2.46,prediction2.58,(RMSEP)and were the,and prediction(RPD)2.50,developed3.58,achieve respectively.rapid this the the quality mod el(MSC-SPA-PLSR)fourcomponents based Fructus,on hyperspectral which technology was approach to rapid and effective detection detection of the of Lycii in Lycii provided a new to the and nondestructive of of Fructus.

The abilities of movie-watching functional connectivity in individual identifications and individualized predictions.

Quite a few studies have been performed based on movie-watching functional connectivity (FC). As compared to its resting-state counterpart, however, there is still much to know about its abilities in individual identifications and individualized predictions. To pave the way for appropriate usage of movie-watching FC, we systemically evaluated the minimum number of time points, as well as the exact functional networks, supporting individual identifications and individualized predictions of apparent traits based on it. We performed the study based on the 7T movie-watching fMRI data included in the HCP S1200 Release, and took IQ as the test case for the prediction analyses. The results indicate that movie-watching FC based on only 15 time points can support successful individual identifications (99.47%), and the connectivity contributed more to identifications were much associated with higher-order cognitive processes (the secondary visual network, the frontoparietal network and the posterior multimodal network). For individualized predictions of IQ, it was found that successful predictions necessitated 60 time points (predicted vs. actual IQ correlation significant at P < 0.05, based on 5,000 permutations), and the prediction accuracy increased logarithmically with the number of time points used for connectivity calculation. Furthermore, the connectivity that contributed more to individual identifications exhibited the strongest prediction ability. Collectively, our findings demonstrate that movie-watching FC can capture rich information about human brain function, and its ability in individualized predictions depends heavily on the length of fMRI scans.

Stability analysis for seed yield of chickpea (Cicer arietinum L.) genotypes by experimental and biological approaches.

A range of environmental factors restricts the production of chickpea; therefore, introducing compatible cultivars to a range of environments is an important goal in breeding programs. This research aims to find high-yielding and stable chickpea genotypes to rainfed condition. Fourteen advanced chickpea genotypes with two control cultivars were cultivated in a randomized complete block design in four regions of Iran during 2017-2020 growing seasons. The first two principal components of AMMI explained 84.6 and 10.0 % of genotype by environment interactions, respectively. Superior genotypes based on simultaneous selection index of ASV (ssiASV), ssiZA, ssiDi and ssiWAAS were G14, G5, G9 and G10; those based on ssiEV and ssiSIPC were G14, G5, G10 and G15 and those based on ssiMASD were G14, G5, G10 and G15. The AMMI1 biplot identified G5, G12, G10 and G9 as stable and high-yielding genotypes. Genotypes G6, G5, G10, G15, G14, G9 and G3 were the most stable genotypes in the AMMI2 biplot. Based on the harmonic mean and relative performance of genotypic values, G11, G14, G9 and G13 were the top four superior genotypes. Factorial regression indicated that rainfall is very important at the beginning and end of the growing seasons. Genotype G14, in many environments and all analytical and experimental approaches, has good performance and stability. Partial least squares regression identified genotype G5 as a suitable genotype for moisture and temperature stresses conditions. Therefore, G14 and G5 could be candidates for introduction of new cultivars.

Characterization of aroma compounds in Rosa roxburghii Tratt using solvent-assisted flavor evaporation headspace-solid phase microextraction coupled with gas chromatography-mass spectrometry and gas chromatography-olfactometry.

Rosa roxburghii Tratt (RRT) has become popular owing to its high vitamin C content. Volatiles are important factors that affect the quality of RRTs and their processed products. In this study, volatile compounds were extracted using headspace-solid phase microextraction (HS-SPME) and solvent-assisted flavor evaporation (SAFE); 143 volatile compounds were identified by gas chromatography-mass spectrometry (GC-MS), and RRT from different origins were well distinguished based on principal component analysis. 45 odor-active components were identified using gas chromatography-olfactometry (GC-O). Through quantitative descriptive analysis (QDA), there were prominent "grassy" and "tea-like" attributes in RRT. Partial least-squares regression (PLSR) revealed that Longli RRT was greatly related to "tea-like" and "woody" attributes. Among the volatiles identified, alcohols and esters were considered the dominant volatile compounds of RRT, 4-methoxy-2,5-dimethyl-3(2H)-furanone was the most prominent compound. This study enriches the flavor chemistry theory of RRT and provides a scientific basis for optimizing the aroma of RRT and its processed products.

Predicting leaf traits across functional groups using reflectance spectroscopy.

Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 103 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems, mainly in eastern Canada. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits such as leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R2 > 0.85; %RMSE < 10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R2  = 0.55-0.85; %RMSE = 12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits such as LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several functional traits from leaf spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.

Hyperspectral Imaging of Adaxial and Abaxial Leaf Surfaces as a Predictor of Macadamia Crop Nutrition.

Tree crop yield is highly dependent on fertiliser inputs, which are often guided by the assessment of foliar nutrient levels. Traditional methods for nutrient analysis are time-consuming but hyperspectral imaging has potential for rapid nutrient assessment. Hyperspectral imaging has generally been performed using the adaxial surface of leaves although the predictive performance of spectral data has rarely been compared between adaxial and abaxial surfaces of tree leaves. We aimed to evaluate the capacity of laboratory-based hyperspectral imaging (400-1000 nm wavelengths) to predict the nutrient concentrations in macadamia leaves. We also aimed to compare the prediction accuracy from adaxial and abaxial leaf surfaces. We sampled leaves from 30 macadamia trees at 0, 6, 10 and 26 weeks after flowering and captured hyperspectral images of their adaxial and abaxial surfaces. Partial least squares regression (PLSR) models were developed to predict foliar nutrient concentrations. Coefficients of determination (R2P) and ratios of prediction to deviation (RPDs) were used to evaluate prediction accuracy. The models reliably predicted foliar nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), copper (Cu), manganese (Mn), sulphur (S) and zinc (Zn) concentrations. The best-fit models generally predicted nutrient concentrations from spectral data of the adaxial surface (e.g., N: R2P = 0.55, RPD = 1.52; P: R2P = 0.77, RPD = 2.11; K: R2P = 0.77, RPD = 2.12; Ca: R2P = 0.75, RPD = 2.04). Hyperspectral imaging showed great potential for predicting nutrient status. Rapid nutrient assessment through hyperspectral imaging could aid growers to increase orchard productivity by managing fertiliser inputs in a more-timely fashion.

Dynamic Zanthoxylum pungency characteristics and their correlation with sanshool composition and chemical structure.

Huajiao (Zanthoxylum) from different regions varies in pungency features. The objective of this study was to explore the reasons for the differences. Temporal check-all-that-apply (TCATA) and time-intensity (TI)) were used to determine time-related pungency features of huajiao and sanshools. The compositions of sanshools in huajiao were measured by high-performance liquid chromatograph (HPLC). TI results revealed that hydroxy-γ-sanshool tingling and numbing duration (1332.00 ± 50.91 and 1020.00 ± 61.19 s, respectively) were about twice that of hydroxy-α-sanshool (720.00 ± 25.92 and 584.00 ± 22.63 s, respectively). Tingling and numbing were not perceived by hydroxy-ß-sanshool and hydroxy-γ-isosanshool. HPLC results showed that HαSS was the main component of huajiao sanshools, representing 71.06 % to 92.90 %. TCATA results revealed the pungency sensations appearance sequence: tingling, salivating, cooling, and burning appeared first, followed by vibrating, and numbing was perceived last. These findings revealed the relationship between the compositions of sanshool and the pungency features of huajiao.

Novel application of 1H NMR spectroscopy coupled with chemometrics for the authentication of dark chocolate.

The applicability of 1H NMR spectroscopy coupled with chemometric in the quality control of dark chocolate was investigated for the first time to detect cocoa-butter equivalents (CBEs) above the allowed limit by European regulation. Blends of chocolate-fats with CBEs in the range 0-50 % were prepared and analyzed by 1H NMR spectroscopy. Datasets composed of peaks' areas or spectral variables (fingerprinting) in glycerol region were tested for the creation of multivariate statistical models. Partial least-squares discriminant analysis (PLS-DA) and regression (PLS-R) methods were used to correctly identify the type of CBE and quantify its concentration respectively. The performances of the models created on the two datasets were evaluated in terms of chemometric indicators and compared. The robustness of models was investigated through the analysis of test sets and random permutation tests. Fingerprinting models revealed fruitful results in classifying and quantifying CBEs in blends demonstrating the applicability of NMR in chocolate quality control.

Estimation of multi-media metal(loid)s around abandoned mineral processing plants using hyperspectral technology and extreme learning machine.

Hyperspectral techniques are promising alternatives to traditional methods of investigating potentially toxic metal(loid) contamination. In this study, hyperspectral technology combined with partial least squares regression (PLSR) and extreme learning machine (ELM) established estimation models to predict the contents of copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), lead (Pb) and tin (Sn) in multi-media environments (mine tailings, soils and sediments) surrounding abandoned mineral processing plants in a typical tin-polymetallic mineral agglomeration in Guangxi Autonomous Region. Four spectral preprocessing methods, Savitzky-Golay (SG) smoothing, continuum removal (CR), first derivative (FD) and continuous wavelet transform (CWT), were used to eliminate noise and highlight spectral features. The optimum combinations of spectral preprocessing and machine learning algorithms were explored, then the estimation models with best accuracy were obtained. CWT and CR were excellent spectral pretreatments for the hyperspectral data regardless of the applied algorithms. The coefficients of determination (R2) of estimation models for the best accuracy of various metals (loid) are as follows: Cu (CWT-ELM:0.85), Zn (CR-PLSR:0.93), As (CWT-ELM: 0.86), Cd (CR-PLSR: 0.89), Pb (CWT-PLSR: 0.75) and Sn (CR-ELM: 0.81). In contrast, ELM models had higher accuracy with R2 > 0.80 (except Cd and Pb). In conclusion, ELM-based spectral estimation models are able to predict metal (loid) concentrations with high accuracy and efficiency, providing a potential new combinatorial approach for estimating toxic metal contamination in multi-media environments.

Characterization of the key nonvolatile metabolites in Cheddar cheese by partial least squares regression (PLSR), reconstitution, and omission.

Cheddar cheese was desirable by consumers worldwide due to its characteristic taste and aroma, but limited information was available about taste contributors. Therefore, sensory profiles of natural Cheddar cheeses were investigated by quantitative descriptive analysis (QDA). Umami, salty, and brothy attributes were the principal differential attributes. Nonvolatile metabolites were analyzed to decipher their taste contributions, of which free amino acids (FAAs) and organic acids recorded the most considerable differences. The key taste compounds were identified by partial least squares regression (PLSR), reconstitution, and omission tests. NaCl and glutamic acid (Glu) were the primary contributors of salty and umami tastes, respectively. Sourness was synergistically affected by organic acids and Glu. Despite their limited taste impacts, the remaining amino acids intensified sourness and saltiness when mixed with Glu. Organic acids (especially in combination) and NaCl exhibited significant taste enhancements. These results provided deep insight into crucial nonvolatile metabolites promoting Cheddar cheese tastes.

Early detection of stripe rust infection in wheat using light-induced fluorescence spectroscopy.

In the current study, the application of fluorescence spectroscopy along with the advanced statistical technique and confocal microscopy was investigated for the early detection of stripe rust infection in wheat grown under field conditions. The indigenously developed Fluorosensor fitted with LED, emitting monochromatic light was used that covered comparatively larger leaf area for recording fluorescence data thus presenting more reliable current status of the leaf. The examined leaf samples covered the entire range of stripe rust disease infection from no visible symptoms to the complete disease prevalence. The molecular changes were also assessed in the leaves as the disease progresses. The emission spectra mainly produce two fluorescence emission classes, namely the blue-green fluorescence (400-600 nm range) and chlorophyll fluorescence (650-800 nm range). The chlorophyll fluorescence region showed lower chlorophyll bands both at 685 and 735 nm in the asymptomatic (early diseased) and symptomatic (diseased) leaf samples than the healthy ones as a result of partial deactivation of PSII reaction centers. The 735 nm chlorophyll fluorescence band was either slight or completely absent in the leaf samples with lower to higher disease incidence and thus differentiate between the healthy and the infected leaf samples. The Hydroxycinnamic acids (caffeic and sinapic acids) showed decreasing trend, whereas the ferulic acid increased with the rise in disease infection. Peak broadening/shifting has been observed in case of ferulic acid and carotenes/carotenoids, with the increase in the disease intensity. While using the LEDs (365 nm), the peak broadening and the decline in the chlorophyll fluorescence bands could be used for the early prediction of stripe rust disease in wheat crop. The PLSR statistical techniques discriminated well between the healthy and the diseased samples, thus showed promise in early disease detection. Confocal microscopy confirmed the early prevalence of stripe rust disease infection in a susceptible variety at a stage when the disease is not detectable visually. It is inferred that fluorescence emission spectroscopy along with the chemometrics aided in the effective and timely diagnosis of plant diseases and the detected signatures provide the basis for remote sensing.

Rapid detection technology of chemical component content in Lycii Fructus based on hyperspectral technology / 中国中药杂志

This Fructus,study including and aimed to construct a rapid and nondestructive detection flavonoid,model betaine,for and of the content vitamin of(Vit four four quality C).index components Lycium barbarum polysaccharide,of inL ycii rawma total and C Hyperspectral data quantitative of terials modelswere powder developed Lycii using Fructus partial were squares effects collected,regression raw based LSR),on the support content vector the above components,the forest least(P regression compared,(SVR),the and effects random three regression(RFR)were algorithms.also The Four spectral predictive commonly data of the materialsand powder were were applied and of spectral quantitative for models reduction.compared.used were pre-processing screened methods feature to successive pre-process projection the raw algorithm data(SPA),noise competitive Thepre-processed for bands using adaptive reweigh ted sampling howed(CARS),the and maximal effects relevance based and raw minimal materials redundancy and(MRMR)were algorithms Following to optimize multiplicative the models.scatter The correction Based resultss(MS that prediction SPA on feature the powder prediction similar.PLSR C)denoising sproposed and integrated for model,screening the the coefficient bands,determination the effect(R_C~2)of(MSC-SPA-PLSR)coefficient was optimal.of on(R_P~2)thi of of calibration flavonoid,and and of all determination greater prediction0.83,L.barbarum inconte nt prediction of polysaccharide,total mean betaine,of Vit C were than smallest In the compared study,root with mean other prediction content squareserror models of the calibration(RMSEC)residual and deviation root squares was error2.46,prediction2.58,(RMSEP)and were the,and prediction(RPD)2.50,developed3.58,achieve respectively.rapid this the the quality mod el(MSC-SPA-PLSR)fourcomponents based Fructus,on hyperspectral which technology was approach to rapid and effective detection detection of the of Lycii in Lycii provided a new to the and nondestructive of of Fructus.