Set Of Predictors Research Articles

Rapid identification of fragrant rice using starch flavor compound via NIR spectroscopy coupled with GC–MS and Badh2 genotyping

The identification of fragrant rice varieties using near-infrared reflectance spectroscopy (NIRS) models has attracted extensive attention from regulatory authorities worldwide. In this study, 138 fragrant and 54 nonfragrant rice varieties were planted in the same region and distinguished using sensory evaluation, gas chromatography–mass spectrometry analysis, and betaine aldehyde dehydrogenase 2 (Badh2) genotyping. Then, the 2-acetyl−1-pyrroline (2-AP) content was assessed based on partial least-squares discriminant (PLS-DA) models generated after 2nd individually or combined with SNV/MSC/smoothing preprocessing successfully classified fragrant rice both in the calibration and predictive sets. Moreover, design of experiments (DoE)-based preprocessing selection was employed as an effective strategy to optimize the calibration models compared with the one variable at a time (OVAT) method. Further, Badh2 genotype sample screening assisted with identifying authentic fragrant rice and guaranteed the NIRS model's prediction accuracy in identifying fragrant rice. In conclusion, the high throughput PLS-DA multivariate method coupled with NIRS data was applied to identify fragrant rice varieties in routine monitoring and was effective, accurate, and rapid.

Qualitative and quantitative assessment of apple quality using bulk optical properties in combination with machine learning and chemometrics techniques

This study aimed to understand the quantitative relationship between the bulk optical properties (BOP), soluble solids content (SSC), and fruit firmness (FF) of apples, along with the qualitative discrimination of apple cultivar and shelf-life. The absorption coefficient (μa) and reduced scattering coefficient (μs′) of 200 apples from four cultivars during 36-days shelf-life were determined using the single integrating sphere technique in 500-1000 nm. Partial least squares regression (PLSR) and random forest (RF) algorithms were used to establish quantitative prediction models for SSC and FF based on the BOP of apples. The results indicated that the PLSR models based on μɑ and μs′ were optimal for quantitative prediction of SSC (R2p=0.749, RMSEP=0.507) and FF (R2p=0.745, RMSEP=0.571), respectively. RF and linear discriminant analysis (LDA) were used to establish qualitative models for discriminating apple cultivar and shelf-life, demonstrating that the RF model based on μɑ and μɑ+ μs′ had the highest accuracy for the determination of apple cultivar and shelf-life, respectively, with the prediction set reaching 93.2 % and 85.7 %. Overall, RF was better than LDA for qualitative discrimination; however, it was less effective than PLSR for quantitative modeling.

Rapid Prediction of Moisture and Ash Content in Sungkai Leaves Herbal Tea (Peronema canescens Jack.) using NIR Spectroscopy

It is imperative to measure the chemical composition of Sungkai leaf herbal tea in order to produce high-quality goods that promote human health. The moisture and ash content of Sungkai leaf herbal tea are critical parameters for assessing the quality of herbal tea. This study aimed to evaluate an NIR spectroscopy method for quickly determining the moisture and ash content of Sungkai leaf herbal tea. Sungkai leaf herbal tea has a moisture content between 3.93% and 7.59%, and an ash content between 3.94% and 5.51%. We developed a calibration model using partial least squares (PLS) with several pretreatment methods. We split the data into calibration and prediction sets and performed an internal random cross-validation. A PLS calibration model with Rp2 = 0.86, a root means square error of prediction (RMSEP) of 0.30 (%), and a residual predictive deviation (RPD) of 2.76, performed exceptionally well at predicting the moisture content when the standard normal variate (SNV) pre-treatment was applied to the NIR spectra. The Savitzky-Golay derivative (a 9-point smoothing window, second-order polynomial, dg2) pre-treatment method also generated the best PLS calibration model for ash content determination, with Rp2 = 0.70, RMSEP = 0.16 (%), and RPD = 1.86. NIR spectroscopy can quickly determine the moisture and ash content of Sungkai leaf herbal tea, as suggested by these findings.

An adaptive prediction method for carbon emissions of power systems containing new energy based on least-squares support vector machine

Aiming at the problem of high volatility and intermittency of new energy-containing power systems, which affects the prediction accuracy of carbon emissions, we study the adaptive prediction method of carbon emissions of a new energy-containing power system based on a least-squares support vector machine. The carbon emission coefficients of the nodes of the new energy-containing power system are determined based on the trend analysis method. Historical carbon emissions and carbon emission coefficients of the power system are collected, and the rough set method is used to simplify the carbon emission attributes and obtain a simplified attribute set for carbon emission prediction. The least-squares support vector mechanism is used to construct the carbon emission prediction model, and the simplified attribute set is used as the input of the carbon emission prediction model. The whale optimization algorithm was selected to determine the optimal parameters of the least-squares support vector machine through the process of encircling prey, hunting behavior, searching for predation, and adaptively optimizing the least-squares support vector machine to output carbon emission prediction results. The experimental results show that the method can accurately predict the carbon emissions of the power system, and the mean square error of the carbon emissions prediction is lower than 7.

Estimation of the soluble solid content of citrus based on the fractional-order derivative and optimal band combination algorithm.

The soluble solid content (SSC) is a primary characteristic index for evaluating the internal quality of citrus fruits. The development of rapid and nondestructive SSC detection techniques can help address the current issues of postharvest quality grading in China's citrus industry. In this study, a total of 261 experimental samples, including 70 Murcott, 91 Clementine, and 100 Navel orange, were divided into prediction and validation sets in a 7:3 ratio. After obtaining the reflection spectra and SSCs, SNV-FOD (Standard Normal Variate-Fractional-Order Derivative) was used to process the spectra, and the optimal band combination algorithm was introduced to select SSC-sensitive bands. Then, the obtained optimal dual-band combination was input into eight regression models for comparison, and the best performing models stacked ensemble models was selected. Finally, the H-ELR (HyperOpt-optimized ensemble learning regression) model, optimized using a Bayesian function, was applied for the effective estimation of SSC for three common citrus varieties in Guangxi, Murcott, Clementine, and Navel oranges. The results show that (1) the SNV-FOD preprocessing method proposed in this study improved the correlation coefficient with the SSC from 0.546 to 0.836 compared to that of the original spectrum, (2) the optimal dual-band combination (969 and 1069nm) constructed by integrating the differential index and 1.2-order derivative yielded the most accurate results (RPD=2.13), and (3) the H-ELR model, based on HyperOpt optimization, achieved good estimated performance (RPD=2.46). PRACTICAL APPLICATION: This research contributes to the development of practical SSC prediction instruments with excellent universality and ease of application.

Reference-based image super-resolution of hyperspectral and red-green-blue image for determination of wheat kernel quality using deep learning networks

In the process of cultivation and harvest, wheat kernel quality is highly susceptible to various factors, such as disease, mildew, atrophy and impurities, and detection of kernel quality is essential to avoid hazard proliferation, facilitate product grading, and ensure food safety. Possessing abundant image and spectral characteristics, hyperspectral imaging (HSI) has gained impressive achievements in kernel quality analysis, but its low spatial resolution limits its detection accuracy. In this study, reference-based image super-resolution (RefSR) of HSI and Red-Green-Blue image was adopted to improve resolution to determine wheat kernel quality using deep learning networks. Firstly, RefSR was conducted by the improved transformer network with dual-branch feature extraction and weighted fusion operation and achieved excellent RefSR with significant resolution improvement, peak signal to noise ratio of 35.521 and structural similarity index of 0.97, outweighing the existing state-of-the-art networks. Then, the reflectance images (RIs) of effective wavelengths (EWs) from generated HSI images were combined with the residual network with a spatial, channel attention and multi-scale residual to determine wheat kernel quality. Precise analysis was achieved with the accuracy in calibration, validation and prediction sets of 100.00%, 95.26% and 92.78%. RefSR provides a novel and efficient approach for obtaining HSI images of high spatial resolution and facilitates the application of HSI in analysis of crop kernels. RIs of several sporadic EWs can be easily acquired and processed, achieving field and rapid kernel detection. Therefore, the proposed method furnishes the efficient, accurate and applicable determination of wheat kernel quality.

Episode model: The functional approach to emotional experiences of music

We present a novel framework for music and emotion research that addresses emotional experiences with music as functional episodes. This framework, called the Episode Model, places the situation and the function of the music for the individual at the centre of the experience and integrates acts of affective self-regulation to our understanding of music as emotional experiences. The model consists of a set of five common and functionally unique episodes of emotional experiences related to music, which are: (1) Enjoyment–Distraction–Relaxation (EDR), (2) Connection–Belonging (CB), (3) Focus–Motivation (FM), (4) Personal Emotional Processing (PEP), and (5) Aesthetic–Interest–Awe (AIA). Each episode type can be characterised by a distinct configuration of six descriptive schemes: (1) core affect and emotion qualia, (2) induction mechanisms, (3) listening modes and agency, (4) reward and exposure, (5) musical meanings, and (6) functional contexts. This framework of episodes and schemes places the functionality of emotions at the forefront of music and emotion research and explains how emotional experiences are situated and functionally constructed. In addition, we provide a set of assumptions and specific predictions to facilitate focussed empirical studies of emotional engagement with music.

Grade Classification of Camellia Seed Oil Based on Hyperspectral Imaging Technology.

To achieve the rapid grade classification of camellia seed oil, hyperspectral imaging technology was used to acquire hyperspectral images of three distinct grades of camellia seed oil. The spectral and image information collected by the hyperspectral imaging technology was preprocessed by different methods. The characteristic wavelength selection in this study included the continuous projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), and the gray-level co-occurrence matrix (GLCM) algorithm was used to extract the texture features of camellia seed oil at the characteristic wavelength. Combined with genetic algorithm (GA) and support vector machine algorithm (SVM), different grade classification models for camellia seed oil were developed using full wavelengths (GA-SVM), characteristic wavelengths (CARS-GA-SVM), and fusing spectral and image features (CARS-GLCM-GA-SVM). The results show that the CARS-GLCM-GA-SVM model, which combined spectral and image information, had the best classification effect, and the accuracy of the calibration set and prediction set of the CARS-GLCM-GA-SVM model were 98.30% and 96.61%, respectively. Compared with the CARS-GA-SVM model, the accuracy of the calibration set and prediction set were improved by 10.75% and 12.04%, respectively. Compared with the GA-SVM model, the accuracy of the calibration set and prediction set were improved by 18.28% and 18.15%, respectively. The research showed that hyperspectral imaging technology can rapidly classify camellia seed oil grades.

Joint Prediction of Sea Clutter Amplitude Distribution Based on a One-Dimensional Convolutional Neural Network with Multi-Task Learning

Accurate modeling of sea clutter amplitude distribution plays a crucial role in enhancing the performance of marine radar. Due to variations in radar system parameters and oceanic environmental factors, sea clutter amplitude distribution exhibits multiple distribution types. Focusing solely on a single type of amplitude prediction lacks the necessary flexibility in practical applications. Therefore, based on the measured X-band radar sea clutter data from Yantai, China in 2022, this paper proposes a multi-task one-dimensional convolutional neural network (MT1DCNN) and designs a dedicated input feature set for the joint prediction of the type and parameters of sea clutter amplitude distribution. The results indicate that the MT1DCNN model achieves an F1 score of 97.4% for classifying sea clutter amplitude distribution types under HH polarization and a root-mean-square error (RMSE) of 0.746 for amplitude distribution parameter prediction. Under VV polarization, the F1 score is 96.74% and the RMSE is 1.071. By learning the associations between sea clutter amplitude distribution types and parameters, the model’s predictions become more accurate and reliable, providing significant technical support for maritime target detection.

Geographical Traceability of Millet by Mid-Infrared Spectroscopy and Feature Extraction

To identify the geographical origin of millet accurately, 36 samples of Guangling millet, Qinzhouhuang millet, Liuseng millet, Qiananhuang millet, and 33 samples of Yuzhou millet were collected. Mid-infrared (mid-IR) spectra of all the samples were obtained. Denoising, standard normal variate (SNV), multiplicative scatter correction (MSC), and normalization were carried out to preprocess the data. Principal component analysis (PCA) was used to reduce the dimension of the data, combined with support vector machine (SVM), and the geographical origin of the five kinds of millet was identified. The recognition accuracy of the training set (99.2%) and the prediction set (98.3%) were highest when using the first 12 principal components, indicating that the established mid-IR spectroscopic identification model was feasible and effective. PCA, window analysis, hierarchical clustering analysis, and SVM were combined to extract the feature information of mid-IR spectra of millet from five producing areas. Five wavenumbers, 1026, 1053, 1685, 1715, and 1744 cm-1, were found to be with small correlation, and the recognition accuracy of the training set and the prediction set based on these five features were 95.8% and 100.0%, respectively. The feature extraction method established here could be used to improve the prediction efficiency of the identification model and provide data support for the analysis of differential components.

Structure-based pose prediction: Non-cognate docking extended to macrocyclic ligands

So-called “cross-docking” is the prediction of the bound configuration of small-molecule ligands that differ from the cognate ligand of a protein co-crystal structure. This is a much more challenging problem than re-docking the cognate ligand, particularly when the new ligand is structurally dissimilar from prior known ones. We have updated the previously introduced PINC (“PINC Is Not Cognate”) benchmark which introduced the idea of temporal segregation to measure cross-docking performance. The temporal set encompasses 846 future ligands for ten targets based on information from the earliest 25% of X-ray co-crystal structures known for each target. Here, we extend the benchmark to include thirteen targets where the bound poses of 128 macrocyclic ligands are to be predicted based on knowledge from structures of bound non-macrocyclic ligands. Performance was roughly equivalent for both the temporally-split non-macrocyclic ligand set and the macrocycle prediction set. Using standard and fully automatic protocols for the Surflex-Dock and ForceGen methods, across the combined 974 non-macrocyclic and macrocyclic ligands, the top-scoring pose family was correct 68% of the time, with the top-two pose families achieving a 79% success rate. Correct poses among all those predicted were identified 92% of the time. These success rates far exceeded those observed for the alternative methods AutoDock Vina and Gnina on both sets.

Rapid on-site multi-indicator detection of Polygonatum cyrtonema Hua by handheld near-infrared spectroscopy with wavelength selection

The rapid, accurate, and on-site quantitative evaluation of the 3 components (total polysaccharide, total flavonoid, and total saponin) of Polygonatum cyrtonema Hua (PCH) is of great significance to ensure the value of its medicine and food, for this purpose, a handheld near-infrared (NIR) spectrometer was used. The raw NIR spectra were preprocessed by seven methods and their combinations, then three kinds of wavelength selection algorithms, the competitive adaptive reweighted sampling (CARS), random frog (RF) sampling, and Monte Carlo uninformative variable elimination (MCUVE) were applied, and the characteristic wavelengths for total polysaccharide, total saponin, and total flavonoid in PCH were obtained, respectively. Subsequently, the partial least squares (PLS) regression models were developed. The results showed that the combination of first derivative and standard normal variate was the optimal pretreatment method, and CARS had the highest performance of the wavelength selection for total polysaccharide and total flavonoid, while RF was the highest for total saponin. With these selected wavelengths, 3 PLS prediction models were built, and their performances were validated with unknown samples, the results showed that the determination coefficient of the prediction set (R2P) of total polysaccharide, total saponin, and total flavonoid were 0.980, 0.948, and 0.942, respectively, and the corresponding root mean square error of prediction set (RMSEP) were 0.162, 0.142, and 0.052. Furthermore, the residual prediction deviations (RPDs) of the three components were all larger than 3, demonstrating the prediction performance of the models was high. Therefore, the three active functional components in PCH could be rapidly determined by a handheld NIR spectrometer, which would be beneficial for ensuring the quality of PCH.

Quantitative Prediction of Acid Value of Camellia Seed Oil Based on Hyperspectral Imaging Technology Fusing Spectral and Image Features.

Acid value (AV) serves as an important indicator to assess the quality of oil, which can be used to judge the deterioration of edible oil. In order to realize the quantitative prediction of the AV of camellia seed oil, which was made from camellia oleifolia, hyperspectral data of 168 camellia seed oil samples were collected using a hyperspectral imaging system, which were related to their AV content measured via classical chemical titration. On the basis of hyperspectral full wavelengths, characteristic wavelengths, and fusing spectral and image features, the quantitative prediction AV models for camellia seed oil were established. The results demonstrating the 2Der-SPA-GLCM-PLSR model fusing spectral and image features stood out as the optimal choices for the AV prediction of camellia seed oil, with the correlation coefficient of calibration set (Rc2) and the correlation coefficient of prediction set (Rp2) at 0.9698 and 0.9581, respectively. Compared with those of 2Der-SPA-PLSR, the Rc2 and Rp2 were improved by 2.11% and 2.57%, respectively. Compared with those of 2Der-PLSR, the Rc2 and Rp2 were improved by 5.02% and 5.31%, respectively. Compared with the model based on original spectrum, the Rc2 and Rp2 were improved by 32.63% and 40.11%, respectively. After spectral preprocessing, characteristic wavelength selection, and fusing spectral and image features, the correlation coefficient of the optimal AV prediction model was continuously improved, while the root mean square error was continuously decreased. The research demonstrated that hyperspectral imaging technology could precisely and quantitatively predict the AV of camellia seed oil and also provide a new environmental method for detecting the AV of other edible oils, which is conducive to sustainable development.

Grade Identification of Strong-flavor Raw Liquor Based on GC-MS Combined with Spearman-KPCA Feature Extraction

Taking different grades of strong-flavor raw liquor as the research object, gas chromatography-mass spectrometry (GC-MS) technology was used to obtain the volatile components mapping data of raw liquor, and Spearman correlation coefficient (Spearman) combined with principal component analysis (PCA) and kernel principal component analysis (KPCA) was used to realize the secondary feature extraction of the GC-MS data, and then combined with the support vector machine (SVM), extreme gradient boosting (XGBoost), and BP neural network to establish the raw liquor grades identification model, respectively. The results show that the prediction accuracy of the grade identification model based on Spearman-KPCA dimensionality reduction data is better, in which the Spearman-KPCA-BP neural network model has the best classification effect, and the accuracy of the correction set and prediction set reaches 99.44% and 96.10%, respectively. Research shows that the principal components extracted based on Spearman-KPCA secondary features can better characterize the characteristic information of different grades of raw liquor. Combined with the BP neural network model, it can effectively realize the identification of different grades of raw liquor. It is an effective method for identifying the grade of raw liquor.

Classification of green tea and quantitative prediction of catechin content based on indicator displacement array sensors

Green tea, renowned for its health benefits and cultural significance, exhibits variations in quality and biochemical composition across different grades and varieties. Traditional quality assessment methods are often labour-intensive and time-consuming, rendering them impractical for rapid analysis. This study employed a sensitive indicator displacement array sensor along with supervised learning algorithms—Extreme learning machine, Support vector machine, Random forest, and Back propagation neural network—to accurately classify green tea varieties and grades, as well as predict catechin content. Sensor components were initially screened based on their responsiveness to epigallocatechin gallate, and subsequently developed using specific indicator-receptor combinations: Pyrocatechol Violet/Fe2+, Alizarin Red S/Phenylboronic Acid, and Bromopyrogallol Red/3-Nitrophenylboronic Acid. Validation was performed through monitoring changes in RGB values and UV–visible spectroscopy. Discrimination among Anji white tea grades was assessed using principal component analysis and further refined through supervised algorithms, achieving high accuracy rates, especially with Random forest and Extreme learning machine models, which excelled in both calibration and prediction sets. These methodologies were extended to classify ten green tea varieties, demonstrating robustness and high accuracy in a multiclass setting. Independent validation confirmed the models’ efficacy in-class differentiation, with most achieving an accuracy of over 90%. Moreover, the quantitative prediction of catechin content was enhanced using nonlinear models, specifically Support vector regression optimized with the Particle swarm optimization algorithm. These models exhibited superior performance compared to linear models, particularly in effectively predicting individual catechin levels. These findings highlight the potential of combining sensor technologies with supervised learning algorithms to enhance tea quality assessment and categorization, establishing a robust framework for future applications in tea quality control.

NIR Spectroscopy for the Online Monitoring of Water and Olive Oil Content in Pomace during the Extraction Process

The main challenge of this scientific work was the implementation on an industrial olive oil extraction plant of an NIR device for the multispectral analysis of pomace to predict the percentage of humidity and oil contained in it. Subsequent to the implementation of the NIR device on the oil extraction line on the solid’s outlet from the decanter, NIRS interaction measurements in the 761–1081 nm region were used to probe the pomace. NIRS calibration models for the prediction of water and oil content in the pomace were obtained and successfully tested and validated. The correlations of calibration results for oil and water content were 0.700 and 0.829, while the correlations of validation were 0.773 and 0.676, respectively. Low values of root mean square error were found for both the prediction and validation set. The results highlight the good robustness of an NIR approach based on a PLS calibration model to monitor the industrial olive oil process. The results obtained are a first step toward the large-scale implementation of NIR devices for monitoring pomace in oil mills. The possibility of knowing the oil lost in the pomace, moment by moment, would open a new frontier towards system control and the sustainability of the olive oil extraction process.

Time Series Prediction of Gas Emission in Coal Mining Face Based on Optimized Variational Mode Decomposition and SSA-LSTM.

The accurate prediction of gas emissions has important guiding significance for the prevention and control of gas disasters in order to further improve the prediction accuracy of gas emissions in the mining face. According to the absolute gas emission monitoring data of the 1417 working face in a coal mine in Shaanxi Province, a GA-VMD-SSA-LSTM gas emission prediction model (GVSL) based on genetic algorithm (GA)-optimized variational mode decomposition (VMD) and sparrow search algorithm (SSA)-optimized long short-term memory (LSTM) is proposed. Firstly, a VMD evaluation standard for evaluating the amount of decomposition loss is proposed. Under this standard, the GA is used to find the optimal parameters of the VMD. Then, the SSA is used to optimize the key parameters of the LSTM to establish a GVSL prediction model. The model predicts each component and finally superimposes the prediction results for each component to obtain the final gas emission result. The results show that the accuracy of the evaluation indexes of the GVSL model and VMD-LSTM model, as well as the SSA-LSTM model and Gaussian process regression (GPR) model, are compared and analyzed horizontally and vertically under three scenarios with prediction sets of 121,94 and 57 groups. The GVSL model has the best prediction effect, and its fitting degree R2 values are 0.95, 0.96, and 0.99, which confirms the effectiveness of the proposed GVSL model for the time series prediction of gas emission in the mining face.

Pruning convolutional neural networks for inductive conformal prediction

Neural network pruning is a popular approach to reducing model storage size and inference time by removing redundant parameters in the neural network. However, the uncertainty of predictions from pruned models is unexplored. In this paper we study neural network pruning in the context of conformal predictors (CP). The conformal prediction framework built on top of machine learning algorithms supplements their predictions with reliable uncertainty measure in the form of prediction sets, under the independent and identically distributed assumption on the data. Convolutional neural networks (CNNs) have complicated architectures and are widely used in various applications nowadays. Therefore, we focus on pruning CNNs and, in particular, filter-level pruning. We first propose a brute force method that estimates the contribution of a filter to the CP’s predictive efficiency and removes those with the least contribution. Given the computation inefficiency of the brute force method, we also propose the Taylor expansion to approximate the filter’s contribution. Furthermore, we improve the global pruning method by protecting the most important filters within each layer from being pruned. In addition, we explore the ConfTr loss function which is optimized to yield maximal CP efficiency in the context of neural network pruning. We have conducted extensive experimental studies and compared the results regarding the trade-offs between predictive efficiency, computational efficiency, and network sparsity. These results are instructive for deploying pruned neural networks with applications using conformal prediction where reliable predictions and reduced computational cost are relevant, such as in safety-critical applications.

Nondestructive intelligent detection of total viable count in pork based on miniaturized spectral sensor

Changes in the freshness of pork due to microbial action during complex transportation and storage indicate an urgent need for in-situ, real-time monitoring techniques for chemical spoilage of meat. This study reported the use of a portable detection device based on a miniaturized visible/near-infrared spectrometer, combined with data noise reduction and machine learning methods, to predict the total viable count (TVC) in pork samples. A rapid detection device for pork TVC was designed based on the miniaturized spectrometer; a spectral preprocessing method based on the resolution of the spectrometer was proposed; the effects of different preprocessing methods on the full-wavelength modeling effect were compared; and four different feature wavelength extraction algorithms were utilized for the feature wavelengths of pork TVC. The modeling effects of different simplified models were compared. The results showed that resolution interval correction combined with standard normal variation was the most effective in full-wavelength modeling, with correlation coefficients of prediction set (RP), root mean square errors in prediction set (RMSEP), and relative percent deviation (RPD) of 0.918, 0.464 (lg CFU/g), and 2.508, respectively; interval random frog − partial least squares regression (iRF-PLSR) had the best predictive ability among all simplified models, the number of wavelengths used in the simplified model was reduced by 85.45% compared with the full-wavelength model. In contrast, the model performance was improved with RP, RMSEP, and RPD of 0.948, 0.392 (lg CFU/g) and 2.970, respectively. The combination of a rational spectral acquisition setup and a data processing methodology, the miniaturized spectrometer showed competitive results with the complex detection system in predicting meat TVC.

Application of terahertz time-domain spectroscopy and chemometrics-based crested porcupine algorithm in identification of different medicinal parts of Angelica sinensis

ObjectiveAngelica sinensis is one of the commonly used Chinese herbal medicine in traditional Chinese medicine clinic, exhibits different pharmacological characteristics due to variations in the content of active ingredients in its head, body, and tail. Therefore, research on the identification methods of different medicinal parts of Angelica sinensis is of great practical significance. Terahertz Time-Domain Spectroscopy (THz-TDS) technology is widely used in the field of nondestructive testing because of its unique electromagnetic wave characteristics. This study explores the feasibility of combining THz-TDS with chemometrics to identify different medicinal parts of Angelica sinensis. MethodsBy comparing the spectral response characteristics of different parts of Angelica sinensis to various optical parameters, the absorption coefficient spectrum in the 0.6–3.0 THz range was selected, and three types of feature extraction algorithms, namely, joint Competitive Adaptive Reweighted Sampling (CARS), Uninformative Variable Elimination (UVE), and Successive Projections Algorithm (SPA), were used to establish the classification models of Extreme Learning Machine (ELM), Random Forest (RF), and Support Vector Machine (SVM) in turn, and optimize the models by using the crown porcupine algorithm (Crested Porcupine Optimizer (CPO) to optimize the model. ResultsThe research results indicate that the CPO optimizer significantly improved the classification accuracy of the models, with the accuracy of the ELM, RF, and SVM models increasing by 4.36%, 1.11%, and 12.22%, respectively. The SPA-CPO-SVM model exhibited the best overall performance, achieving accuracies of 96.11% and 97.96% on the prediction and training sets, respectively, while the number of input features was only 5% of the total feature set. ConclusionThe results show that the fully joint feature extraction strategy and optimization algorithm can play a powerful synergistic effect in model construction, confirming the feasibility of THz-TDS technology to correctly identify different medicinal parts of Angelica sinensis, and providing an important reference for the application of terahertz technology in the identification of Chinese herbal medicines.