Variable Elimination Research Articles

Phenotype control and elimination of variables in Boolean networks

Phenotype control and elimination of variables in Boolean networks

A three-stage wavelength selection algorithm for near-infrared spectroscopy calibration

The near-infrared spectral data is highly high dimensional and contains redundant information, it is necessary to identify the most representative characteristic wavelengths before modeling to improve model accuracy and reliability. At present, there are many methods for selecting the characteristic wavelengths of NIR spectroscopy, but the collinearity among wavelengths is still a main issue that leads to poor model effects. Therefore, this study proposes a three-stage wavelength selection algorithm (Stage III) to reduce redundancy in NIR spectral data and collinearity between wavelength variables, resulting in a simpler and more accurate predictive model. The research uses a public NIR data set of corn samples as its subject. Initially, the wavelengths with the higher correlation coefficients are chosen after calculating the relationship coefficients between every wavelength vector and the concentration vector. On this basis, the correlation coefficients between the vectors of each wavelength point are calculated, and those wavelength points with smaller correlation coefficients with other wavelength points are selected. Ultimately, the stepwise regression analysis selects the wavelengths that provide substantial value to the model as the variables for modeling, leading to the development of a multiple linear regression model. The results show that the model using the three-stage wavelength selection algorithm outperforms those using the full spectrum, Stages I and Stage II, and the coefficient of determination of the test set of the Stage III-MLR model achieved an accuracy of 0.9360. Instead of the successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS), Stage III is better in the model prediction accuracy. Therefore, the three-stage wavelength selection algorithm is an effective wavelength selection algorithm that can effectively model NIR spectroscopy, reduce the collinearity between the wavelength variables, simplify the complexity of the model, and improve the prediction precision of the model.

Patterns and Predictors of Recurrence After Curative Resection of Colorectal Liver Metastasis (CRLM).

Our study aims to determine the predictors and patterns of relapses after curative colorectal liver metastasis (CRLM) resection. A single-centre, retrospective study of CRLM patients operated between 2010 and 2022 was performed. The site of first recurrence was either hepatic (marginal (≤ 1cm) or extramarginal), extrahepatic, or both. Factors that predicted relapse patterns and overall survival were determined by multivariable Cox regression analysis with backward elimination of variables. The study consisted of 258 patients, with a similar proportion of synchronous (144; 56%) and metachronous(114; 43%) metastasis. At a 43-month median follow-up, 156 patients (60.4%) developed recurrences with 33 (21.1%) in the liver, 62(24.03%) extra-hepatic recurrences, and 58 (22.48%) having both. Isolated marginal liver relapses were seen in seven (9.89%) liver recurrence patients. The median overall and relapse-free survivals were 38months (30-54) and 13months (11-16), respectively. The 3-year liver-relapse-free survival was 54.4% (44.9-60.6). Size of liver metastases > 5cm (HR 2.06 (1.34-3.17), involved surgical margins (HR 2.16 (1.27-3.68)), and adjuvant chemotherapy (HR 1.89 (1.07-3.35)) were predictors of hepatic recurrences. Node positivity of primary (HR 1.61 (1.02-2.56)), presence of baseline extra-hepatic metastases (HR 0.30 (0.18-0.51)), size of liver metastases > 5cm (HR 2.02 (1.37-2.99)), poorly differentiated histology (HR 2.25 (1.28-3.49)), presence of LVI (HR 2.25 (1.28-3.94)), and adjuvant chemotherapy (HR 2.15 (1.28-3.61)) were predictors of extra-hepatic recurrences. The study found majority relapses occurred at extrahepatic sites whilst isolated marginal recurrences were few. The consistent predictors of recurrence were size and inability to deliver adjuvant therapy. A tailored adjuvant therapy might improve outcomes after liver metastasectomy in colorectal cancers.

Determination of total protein and sugar content in soy-based beverages using variable selection methods applied to ATR-FTIR spectroscopy

This study explores the potential of using ATR-FTIR coupled with partial least squares (PLS) and random forest (RF) for predicting total protein (TP), total sugar (TS), reducing sugars (RS), and non-reducing sugars (NRS) in soy-based beverages (SBBs). Employing variable selection techniques such as interval PLS (iPLS), synergy interval PLS (siPLS), uninformative variable elimination (UVE), ordered predictors selection (OPS), bat algorithm (BA), genetic algorithm (GA), and particle swarm optimization (PSO). The OPS-PLS emerges as the optimal model for TS prediction, yielding an RMSEP of 0.114 wt% and R2p of 0.934. GA-PLS excels in TP, RS, and NRS prediction, achieving RMSEP values of 0.024 wt%, 0.273 wt%, and 0.339 wt%, with corresponding R2p values of 0.880, 0.847, and 0.450. In RF modeling, GA-RF is identified as the superior model for all properties. These findings underscore the potential of regression models and ATR-FTIR as effective and environmentally friendly tools for analyzing SBB composition.

Detection of early bruises in plum using hyperspectral imaging combination with machine learning algorithm

Early detection of plum bruising is important in the online postharvest quality sorting process. This paper explores the rapid detection of bruises in plum at five stages (1, 3, 6, 24, and 48 h after bruising) using hyperspectral imaging system. Spectral preprocessing was performed using five methods (unit vector normalization, multiplicative scattering correction, standard normal variate, detrending, and baseline). The support vector machine was established to discriminate the spectral samples of healthy and bruised plums at stage 1 on full wavelengths. The results indicated that the spectral data pretreated by multiple scattering correction yielded better results. The characteristic wavelengths of the spectra were selected by six algorithms (random frog, competitive adaptive reweighted sampling, variable combination population analysis, successive projection algorithm, uninformative variable elimination and bootstrapping soft shrinkage). Subsequently, the support vector machine was established at stage 1 based on these selected characteristic wavelengths. Comparing the results of models, the support vector machine based on the characteristic wavelengths selected by competitive adaptive reweighted sampling generated a satisfied effect with an accuracy of 95% for the calibration set and 98% for the prediction set. This model was selected for bruise detection in plums at the residual stage. The characteristic wavelength grayscale images selected based on the model were used to successfully visualize the plum bruise regions in five stages using minimum noise fraction transformation and the principal component analysis method. The results showed that the hyperspectral imaging technique combined with machine learning algorithm could be used to identify plum bruises at different stages. This study contributes to the development of an online detection system for bruises in plum.

Aggregation and assessment of grape quality parameters with visible-near-infrared spectroscopy: Introducing a novel quantitative index

Grape quality is generally assessed by measuring individual indicators rather than utilizing a comprehensive metric. A flexible approach to aggregating multiple quality-related parameters of grapes is introduced in this study, and an aggregative index was proposed for the synthetical evaluation of grape quality. First, visible-near-infrared (Vis-NIR) spectral data of two grape varieties were obtained, and the physical and chemical quality indicators were measured as reference standards. The variation trends and correlations of the indicators were analyzed, and an aggregative quality indicator (AQI) was proposed using the min-max normalization and factor analysis methods. The maturity stages of grapes were categorized by analyzing the AQI values. The spectral data underwent preprocessing using multiplicative scatter correction (MSC), Savitzky-Golay smoothing (SG), first-order derivative (FD), SG+FD and de-trending (DT) methods. Feature wavelengths were extracted using uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the UVE-CARS algorithms. Three regression models for the AQI were established based on partial least squares regression (PLSR), support vector machine regression (SVR) and convolutional neural network (CNN). Results indicated that the PLSR combined with the FD+SG preprocessing and CARS-selected feature wavelengths achieved optimal performance for Cabernet Sauvignon grapes, with the coefficient of determination for the prediction set (Rp2)=0.972, for the calibration set (Rc2)=0.995, the root mean square error of prediction (RMSEP)=0.040 and residual predictive deviation (RPD)=6.064. In addition, for the Muscat Kyoho grape, CNN combined with DT preprocessed full-spectrum data yielded the best prediction for the AQI, with Rp2=0.989, Rc2=0.981, RMSEP=0.103 and RPD=9.482. This research demonstrated that the non-destructive prediction of grape aggregative qualities can be achieved through the combination of Vis-NIR spectroscopy and a chemometric analysis, offering a practical and comprehensive metric for determining grape maturity stages and optimal harvest times. This approach is particularly valuable for the wine industry, as it facilitates better decision-making and quality control.

A Sensitive SERS Sensor Combined with Intelligent Variable Selection Models for Detecting Chlorpyrifos Residue in Tea.

Chlorpyrifos is one of the most widely used broad-spectrum insecticides in agriculture. Given its potential toxicity and residue in food (e.g., tea), establishing a rapid and reliable method for the determination of chlorpyrifos residue is crucial. In this study, a strategy combining surface-enhanced Raman spectroscopy (SERS) and intelligent variable selection models for detecting chlorpyrifos residue in tea was established. First, gold nanostars were fabricated as a SERS sensor for measuring the SERS spectra. Second, the raw SERS spectra were preprocessed to facilitate the quantitative analysis. Third, a partial least squares model and four outstanding intelligent variable selection models, Monte Carlo-based uninformative variable elimination, competitive adaptive reweighted sampling, iteratively retaining informative variables, and variable iterative space shrinkage approach, were developed for detecting chlorpyrifos residue in a comparative study. The repeatability and reproducibility tests demonstrated the excellent stability of the proposed strategy. Furthermore, the sensitivity of the proposed strategy was assessed by estimating limit of detection values of the various models. Finally, two-tailed paired t-tests confirmed that the accuracy of the proposed strategy was equivalent to that of gas chromatography-mass spectrometry. Hence, the proposed method provides a promising strategy for detecting chlorpyrifos residue in tea.

Discrimination of tea seed oil adulteration based on near-infrared spectroscopy and combined preprocessing method

Near-infrared spectroscopy and chemometrics was used to qualitatively distinguish the types of adulterated oils in binary adulteration of tea seed oil in this study. To address the limitations of a single preprocessing method, nine preprocessing methods from four categories were combined, and the impact of preprocessing method order on model accuracy was assessed. Additionally, variable iterative space shrinkage approach (VISSA), interval combinatorial optimization (ICO), and uninformative variables elimination (UVE) were used to screen characteristic wavelengths. Subsequently, a discriminative model for tea seed oil adulteration was constructed using two strategies. The results indicate that the order of preprocessing methods significantly influences model accuracy, and combining preprocessing methods can effectively enhance model accuracy. All three characteristic wavelength selection methods effectively screened characteristic variables. Both two strategies demonstrate good discriminant capabilities for binary adulteration in tea seed oil. In strategy 1, identification accuracies for the calibration, prediction and external datasets are 98.67 %, 100 % and 94.44 %, respectively. In strategy 2, identification accuracies for the calibration, prediction and external datasets are 100 %, 98 % and 94.44 %, respectively. Therefore, integrating NIRS with combined preprocessing and variable screening can effectively discern the types of adulterated oils in tea seed oil, serving as a potent detection tool.

A personal thermal comfort model based on causal artificial intelligence: a physiological sensor-enabled causal identifiability.

Personal thermal comfort affects occupants' health, well-being, and productivity. Its satisfaction is subjective, based on individual characteristics and dynamic environments, and challenging to understand, requiring predicted outcomes and explanations of how and why the outcomes happen. This research fulfills this concern using a personal thermal comfort model based on causal artificial intelligence. It encodes personal thermal comfort satisfaction based on a new causal-and-effect framework to connect the human mind and environmental factors. Random variables encode relevant factors, and the structural causal model performs cause-and-effect relationships. The do-calculus (e.g., d-separated and d-connected) draws the common sense of the model based on causal structure representation, contributing to a human-intelligent understanding. A directed acyclic graph and exact-inference-based variable elimination quantify the model parameters based on real-world observational data. The strength of causal relationships is verified based on causal odd ratio, causal sensitivity, and causal impact. The results highlight that our proposed model can encode physiological and physical factors to predict and explain personal thermal comfort satisfaction. It can predict and explain such satisfaction reasonably and robustly, converging to human-like interpretation. It can be applied to intelligent systems to understand personal thermal comfort.

Early detection of gray blight in tea leaves and rapid screening of resistance varieties by hyperspectral imaging technology.

Gray blight (GB) is a significant disease of tea leaves, posing a severe threat to both the yield and quality. In this study, the process of leaf infection by a pathogenic isolate of the GB disease (DDZ-6) was simulated. Hyperspectral images of normal leaves, infected leaves without symptoms, and infected leaves with mild and moderate symptoms were collected. Combining convolution neural network (CNN), long short-term memory (LSTM), and support vector machine (SVM) algorithms, the early detection model of GB disease, and the rapid screening model of resistant varieties were established. The generality of this method was verified by collecting datasets under field conditions. The visible red-light band demonstrated a pronounced responsiveness to GB disease, with three sensitive bands identified through rigorous screening processes utilizing uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the successive projections algorithm (SPA). The 693, 727, and 766 nm bands emerged as highly sensitive indicators of GB. Under ideal conditions, the CARS-LSTM model excelled in early detection of GB, achieving an accuracy of 92.6%. However, under field conditions, the combination of 693 and 727 nm bands integrated with a CNN provided the most effective early detection model, attaining an accuracy of 87.8%. For screening tea varieties resistant to GB, the SPA-LSTM model excelled, achieving an accuracy of 82.9%. This study provides a core algorithm for a GB disease instrument with detection capabilities, which is of great importance for the early prevention of GB disease in tea plantations. © 2024 Society of Chemical Industry.

Detection of early collision and compression bruises for pears based on hyperspectral imaging technology

Early detection of bruising is one of the major challenges in postharvest quality sorting processes for pears. In this study, visible/near infrared (VIS/NIR) hyperspectral imaging (400–1000 nm) was utilized for early detection of pear bruise type and timing (1, 12, and 24 h post-bruise). Spectral images of nonbruised and mechanically bruised pears (collision and compression) were captured at these intervals for modeling. Spectral data was processed using principal component analysis (PCA) and uninformative variable elimination (UVE) to select optimum wavelengths. Classification models were then built using an extreme learning machine (ELM) and support vector machine (SVM), and compared with a model combining genetic algorithm, sooty tern optimization algorithm, and SVM (STOA-GA-SVM). For PCA-ELM, UVE-ELM, PCA-SVM, and UVE-SVM models, the calibration set accuracies were 98.99%, 98.98%, 96.94%, and 99.23% respectively. And the validation set accuracies were 89.29%, 87.97%, 88.78%, and 88.78% respectively. The STOA-GA-SVM model shows the best performance, and the accuracy of the calibration set and validation set is determined to be 97.19% and 92.86%, respectively. This study shows that the use of the VIS/NIR hyperspectral imaging technique combined with the STOA-GA-SVM algorithm is feasible for the rapid and nondestructive identification of the bruise type and time for pears.

Research on Outgoing Moisture Content Prediction Models of Corn Drying Process Based on Sensitive Variables

Accurate prediction of outgoing moisture content is the key to achieving energy-saving and efficient technological transformation of drying. This study relies on a grain drying simulation experiment system which combined counter and current drying sections to design corn kernel drying experiments. This study obtains 18 kinds of temperature and humidity variables during the drying process and uses Uninformative Variable Elimination (UVE) method to screen sensitive variables affecting the outgoing moisture content. Subsequently, six prediction models for the outgoing corn moisture content were developed, innovatively incorporating Multiple Linear Regression (MLR), Extreme Learning Machine (ELM), and Long Short-Term Memory (LSTM). The results show that eight sensitive variables have been screened to predict the moisture content of outgoing corn. The sensitive variables effectively reduced the redundancy and multicollinearity of data in the MLR model and improved the coefficient of determination (R2) of ELM and LSTM models by 0.02 and 0.05. The MLR prediction model established based on the full set of temperature and humidity data has an R2 of 0.910 and a root-mean-square error (RMSE) of 0.881%, while the UVE-ELM and UVE-LSTM prediction models achieve a better fitting effect and prediction accuracy. The UVE-LSTM model is set with a batch size of 30, a learning rate of 0.01, and 100 iterations. For the training set of UVE-LSTM, the R2 value is 0.931 and the RMSE value is 0.711%. The UVE-ELM model, with sigmoid as the activation function and 14 neurons configured, runs fast and has the best prediction accuracy. The R2 values of UVE-ELM training set and validation set are 0.943 and 0.946, respectively, and the RMSEs are 0.544% and 0.581%. The models proposed in this study provide data reference and technical support for process optimization and automation control of the corn drying process.

Quality assessment of Gastrodia elata Blume (Tianma) based on Vis-NIR spectroscopy: Discrimination of harvest times and prediction of quality indicator contents

Gastrodia elata Blume (Tianma) is widely cultivated and consumed as a food and herbal material in China. With its increasing use as a functional food, Tianma is gaining popularity in the market. However, during the cultivation process, the harvest time affects its maturity and storage time, while the total content of gastrodin (GAS) and p-hydroxybenzyl alcohol (HA) affects its nutritional value, making it difficult to control the quality of Tianma. In this study, a combination of visible-near-infrared (Vis-NIR) spectroscopy and chemometrics was used to identify the harvest time and determine the quality indicator content. A Vis-NIRRamanNet modeling method based on the architecture of RamanNet was proposed and compared with harvest time discrimination models constructed using partial least squares discriminant analysis (PLS-DA), k-nearest neighbor (KNN), support vector machine (SVM), and one-dimensional convolutional neural network (1D-CNN). The results demonstrated that Vis-NIRRamanNet exhibited superior performance, achieving accuracy rates of 0.995 and 0.973 on the training and testing sets, respectively. Subsequently, Vis-NIR spectroscopy combined with support vector regression (SVR) was employed for quantitative analysis of quality indicator content. Wavelength selection methods such as uninformative variable elimination (UVE), genetic algorithm (GA), and competitive adaptive reweighted sampling (CARS) were utilized to optimize the model. The results indicated that the CARS-SVR model provided the best prediction of quality indicator content, with root mean square error of prediction (RMSEP) and coefficient of determination (Rp2) values of 0.257 and 0.901, respectively. Therefore, the combination of Vis-NIR spectroscopy and chemometrics shows significant potential in assisting the selection of high-quality Tianma.

Decomposition-based Synthesis for Applying Divide-and-Conquer-like Algorithmic Paradigms

Algorithmic paradigms such as divide-and-conquer (D&C) are proposed to guide developers in designing efficient algorithms, but it can still be difficult to apply algorithmic paradigms to practical tasks. To ease the usage of paradigms, many research efforts have been devoted to the automatic application of algorithmic paradigms. However, most existing approaches to this problem rely on syntax-based program transformations and thus put significant restrictions on the original program. In this article, we study the automatic application of D&C and several similar paradigms, denoted as D&C-like algorithmic paradigms, and aim to remove the restrictions from syntax-based transformations. To achieve this goal, we propose an efficient synthesizer, named AutoLifter , which does not depend on syntax-based transformations. Specifically, the main challenge of applying algorithmic paradigms is from the large scale of the synthesized programs, and AutoLifter addresses this challenge by applying two novel decomposition methods that do not depend on the syntax of the input program, component elimination and variable elimination , to soundly divide the whole problem into simpler subtasks, each synthesizing a sub-program of the final program and being tractable with existing synthesizers. We evaluate AutoLifter on 96 programming tasks related to six different algorithmic paradigms. AutoLifter solves 82/96 tasks with an average time cost of 20.17 s, significantly outperforming existing approaches.

Rapid and High-Performance Analysis of Total Nitrogen in Coco-Peat Substrate by Coupling Laser-Induced Breakdown Spectroscopy with Multi-Chemometrics

Nitrogen is an important nutrient element for crop growth. Rapid and accurate acquisition of nitrogen content in cultivation substrate is the key to precise fertilization. In this study, laser-induced breakdown spectroscopy (LIBS) was used to detect the total nitrogen (TN) of coco-peat substrate. A LIBS spectrum acquisition system was established to collect the spectral line signal of samples with wavelengths ranging from 200 nm to 860 nm. Synergy interval partial least squares (Si-PLS) algorithm and elimination of uninformative variables (UVE) algorithm were used to select the spectral data of TN characteristic lines in coco-peat substrate. Univariate calibration curve and partial least squares regression (PLSR) were used to build mathematical models for the relationship between the spectral data of univariate characteristic spectral lines, full variables and screened multi-variable characteristic spectral lines of samples and reference measurement values of TN. By comparing the detection performance of calibration curves and multivariate spectral prediction models, it was concluded that UVE was used to simplify the number of spectral input variables for the model and PLSR was applied to construct the simplest multivariate model for the measurement of TN in the substrate samples. The model provided the best measurement performance, with the calibration set determination coefficient () and calibration set root mean square error (RMSEC) values of 0.9944 and 0.0382%, respectively; the prediction set determination coefficient () and prediction set root mean square error (RMSEP) had values of 0.9902 and 0.0513%, respectively. These results indicated that the combination of UVE and PLSR could make full use of the variable information related to TN detection in the LIBS spectrum and realize the rapid and high-performance measurement of TN in coco-peat substrate. It would provide a reference for the rapid and quantitative assessment of nutrient elements in other substrate and soil.

Detection of dried jujube from fresh jujube with different variety and maturity after hot air drying based on hyperspectral imaging technology

The consistent quality of dried jujube has a significant impact on its economic value and consumer acceptance. Currently, the evaluation of dried jujube quality heavily relies on agricultural experts and advanced instrument analysis, which is time costly and is limited by many uncertainties. This study aims to develop a rapid and accurate method for classifying dried jujube produced from fresh jujube at different stages of maturity using hyperspectral imaging. Dried jujube samples were collected from two varieties (Zhongyang Muzao jujube and Yuanling jujube) in Hebei Province, representing two maturity stages: Kimri and Rutab. Linear partial least squares-discriminant analysis (PLS-DA), K-Nearest Neighbor (KNN) and support vector machine (SVM)algorithms were combined with six pre-processing methods. the pretreatment spectra of Area Naturalization (AN), Baseline and Multiple scattering correction (MSC) were selected for further analysis. Successive projections algorithm (SPA), Competitive adaptive reweighted sampling （CARS）and Uninformative Variables Elimination (UVE)were combined to select characteristic variables. A total of 16 characteristic variables were selected by AN-UVE-SPA-SVM model. This model achieved the highest accuracies of 96.0 % and 93.1 % in the final discriminant accuracy for the calibration and Validation sets, respectively. The hardness, soluble solid content (SSC), and moisture of two categories of dried jujube derived from fresh jujube at various stages of maturity were analyzed. The results showed that dried jujube produced from fresh jujubes at the Rutab stage were more likely to exhibit characteristics of high SSC and low hardness' compared to those produced at the Kimri stage. The partial least squares regression (PLSR) model established based on characteristic wavelengths extracted by AN-UVE-SPA showed the best results in predicting SSC and moisture. This study showcased the feasibility of identifying and classifying dried jujube using hyperspectral imaging technology, laying a solid foundation for further research in the rapid non-destructive testing of dried fruits and vegetables through the application of hyperspectral technology.

Potential of hyperspectral imaging for nondestructive determination of α-farnesene and conjugated trienol content in ‘Yali’ pear

The sesquiterpene α-farnesene and its corresponding oxidation products, namely conjugated trienols (CTols) is well known to be correlated with the development of superficial scald, a typical physiological disorder after a long term of cold storage in pear fruit. In this work, hyperspectral imaging (HSI) technology was used for nondestructive predicting of α-farnesene and CTols [CT258, CT281 and CT(281-290)] content in ‘Yali’ pear. In order to obtain the best performance of calibration model and simplify the calibration model further, various preprocessing methods together with their combinations and different wavelength selection algorithms, including successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE), were investigated and compared based on linear partial least square regression (PLSR) and nonlinear least square support vector machine (LS-SVM) models, respectively. In conclusion, compared to the PLSR models, the results of LS-SVM models based on original and preprocessing methods performed better for the prediction of α-farnesene and CTols, while the performance of LS-SVM models based on the selected characteristic wavelengths were worse. For α-farnesene, the best result was obtained by LS-SVM model based on MSC-FD pretreatment with the RPD value of 2.6, Rp = 0.925 and RMSEP = 4.387 nmol cm−2. And for CTols, CT281 performed better compared with CT258 and CT(281-290), achieving the result with RPD = 2.4, Rp = 0.913 and RMSEP = 2.734 nmol cm−2 based on LS-SVM model combined with SD pretreatment. The overall results illustrated HSI technology could be used for rapid and nondestructive prediction of α-farnesene and CTols in ‘Yali’ pear, which would be helpful for supporting postharvest decision systems.

Flexible optimization of variables based on exponential and linear attenuation elimination-binary dragonfly algorithm in near infrared spectroscopic analysis

Single Binary Dragonfly Algorithm (Single-BDA) which is an intelligent optimization algorithm, normally needs more calculation time and often obtains unrobust variables. In this study, an Exponential and Linear Attenuation Elimination-Binary Dragonfly Algorithm (ELAE-BDA) is proposed to overcome the problems by combining exponential and linear attenuation functions. The algorithm can quickly eliminate a part of the variables with large RMSECV in the iterative process through the exponential attenuation function first, and use the linear attenuation function at the end of the iteration for meticulous elimination of invalid variables. It avoids the important variables being deleted unexpectedly, greatly improves the running speed of the algorithm, reduces the randomness of results, and significantly improves the analysis ability of the model. Three near-infrared spectral datasets are used as research objects to evaluate the performance of the ELAE-BDA algorithm, and 6 wavelength selection algorithms including Single-BDA, GA, CARS, MC-UVE, SCARS, and fiPLS are used for comparison. The results show that the ELAE-BDA algorithm can obtain results with almost the smallest number of wavelengths and the highest stability. The established PLSR models have the best prediction with the lowest RMSECV and RMSEP, indicating that the proposed algorithm can provide a more accurate and effective results of wavelength optimization for near-infrared spectral analysis.

Rapid qualitative and quantitative analysis of benzo(b)fluoranthene (BbF) in shrimp using SERS-based sensor coupled with chemometric models

Benzo(b)fluoranthene (BbF), a polycyclic aromatic hydrocarbon (PAH), is a carcinogenic contaminant of concern in seafood. This study developed a simple, rapid, sensitive, and cost-effective surface-enhanced Raman scattering (SERS) sensor (AuNPs) coupled with chemometric models for detecting BbF in shrimp samples. Partial least squares (PLS) regression models were optimized using uninformative variable elimination (UVE), bootstrapping soft shrinkage (BOSS), and competitive adaptive reweighted sampling (CARS). Qualitative analysis was performed using principal component analysis (PCA), linear discriminant analysis (LDA), and k-nearest neighbors (KNN) to differentiate between BbF-contaminated and uncontaminated shrimp samples. The SERS-sensor exhibited excellent sensitivity (LOD = 0.12 ng/mL), repeatability (RSD = 6.21%), and anti-interference performance. CARS-PLS model demonstrated superior predictive ability (R2 = 0.9944), and qualitative analysis discriminated between contaminated and uncontaminated samples. The sensor's accuracy was validated using HPLC, demonstrating the ability of the SERS-sensor coupled with chemometrics to rapidly and reliably detect BbF in shrimp samples.

Online Detection of Dry Matter in Potatoes Based on Visible Near-Infrared Transmission Spectroscopy Combined with 1D-CNN

More efficient resource utilization and increased crop utilization rate are needed to address the growing demand for food. The efficient quality testing of key agricultural products such as potatoes, especially the rapid testing of key nutritional indicators, has become an important strategy for ensuring their quality and safety. In this study, visible and near infrared (Vis/NIR) transmittance spectroscopy (600–900 nm) was used for the online analysis of multiple quality parameters in potatoes. The study concentrated on comparing three one-dimensional convolutional neural network (1D-CNN) models, specifically, the fine-tuned DeepSpectra, the fine-tuned 1D-AlexNet, and classic CNN, with UVE-PLS (uninformative variable elimination–partial least squares) models. These models utilized spectral data for the real-time detection of dry matter (DM) content in potatoes. To address the challenges posed by limited data from Vis/NIR, this study strategically implemented data augmentation techniques. This approach significantly enhanced the robustness and generalization capabilities of the models. The 1D-AlexNet and DeepSpectra models achieved 0.934 and 0.913 R2P and 0.0603 and 0.0695 g/100 g RMSEP for DM, respectively. Compared to UVE-PLS, the R2P value improved by 21.31% (0.770 to 0.934) for the 1D-AlexNet model and 18.64% (0.770 to 0.913) for the DeepSpectra model. The RMSEP value was reduced by 47.31% (0.114 to 0.0603) for 1D-AlexNet, and 39.30% (0.114 to 0.0695) for the DeepSpectra model. As a result, this study would be helpful for researching the online Vis/NIR transmission determination of potato DM using deep learning. These results highlighted the immense potential of employing specific spectral features in deep-learning models for a more precise and efficient online assessment of agricultural quality. This advancement provided some insight and reference for further contributing to the evolution of more targeted and efficient quality assessment methods in agricultural products.