Savitzky-Golay Smoothing Research Articles

Mango quality prediction based on near-infrared spectroscopy using multi-predictor local polynomial regression modeling

Background pH and total soluble solids (TSS) are important quality parameters of mangoes; they represent the acidity and sweetness of the fruit, respectively. This study predicts the pH and TSS of intact mangoes based on near-infrared (NIR) spectroscopy using multi-predictor local polynomial regression (MLPR) modeling. Herein, the prediction performance of kernel partial least square regression (KPLSR), support vector machine regression (SVMR), and MLPR is compared. Methods For this purpose, 186 intact mango samples at three different maturity stages are used. Prediction models are built using MLPR, KPLSR, and SVMR based on untreated and treated spectra. The best regression model for predicting pH is MLPR based on Gaussian filter smoothing spectra. Moreover, the TSS value is more accurately predicted using MLPR based on Savitzky–Golay smoothing. Results The findings reveal that MLPR is highly accurate in estimating the pH and TSS of mangoes, with mean absolute percentage error (MAPE) values less than 10 %. In addition, the MLPR model has the best predictive performance with the lowest Mean Squared error (MSE) and root mean squared error (RMSE) values and the highest R2 value. Conclusions The use of NIR spectroscopy in combination with multi-predictor local polynomial regression could provide a quick and non-destructive technique for predicting mango quality. Thus, the results of this study help support sustainable production as a sustainable development goal.

Non-Destructive Near-Infrared Technology for Efficient Cannabinoid Analysis in Cannabis Inflorescences.

In the evolving field of cannabis research, scholars are exploring innovative methods to quantify cannabinoids rapidly and non-destructively. This study evaluates the effectiveness of a hand-held near-infrared (NIR) device for quantifying total cannabidiol (total CBD), total delta-9-tetrahydrocannabinol (total THC), and total cannabigerol (total CBG) in whole cannabis inflorescences. Employing pre-processing techniques, including standard normal variate (SNV) and Savitzky-Golay (SG) smoothing, we aim to optimize the portable NIR technology for rapid and non-destructive cannabinoid analysis. A partial least-squares regression (PLSR) model was utilized to predict cannabinoid concentration based on NIR spectra. The results indicated that SNV pre-processing exhibited superior performance in predicting total CBD concentration, yielding the lowest root mean square error of prediction (RMSEP) of 2.228 and the highest coefficient of determination for prediction (R2P) of 0.792. The ratio of performance to deviation (RPD) for total CBD was highest (2.195) with SNV. In contrast, raw data exhibited the least accurate predictions for total THC, with an R2P of 0.812, an RPD of 2.306, and an RMSEP of 1.651. Notably, total CBG prediction showed unique characteristics, with raw data yielding the highest R2P of 0.806. SNV pre-processing emerges as a robust method for precise total CBD quantification, offering valuable insights into the optimization of a hand-held NIR device for the rapid and non-destructive analysis of cannabinoid in whole inflorescence samples. These findings contribute to ongoing efforts in developing portable and efficient technologies for cannabinoid analysis, addressing the increasing demand for quick and accurate assessment methods in cannabis cultivation, pharmaceuticals, and regulatory compliance.

Enhancing real-time cell culture monitoring: Automated Raman model optimization with Taguchi method.

Raman spectroscopy has found widespread usage in monitoring cell culture processes both in research and practical applications. However, commonly, preprocessing methods, spectral regions, and modeling parameters have been chosen based on experience or trial-and-error strategies. These choices can significantly impact the performance of the models. There is an urgent need for a simple, effective, and automated approach to determine a suitable procedure for constructing accurate models. This paper introduces the adoption of a design of experiment (DoE) method to optimize partial least squares models for measuring the concentration of different components in cell culture bioreactors. The experimental implementation utilized the orthogonal test table L25(56). Within this framework, five factors were identified as control variables for the DoE method: the window width of Savitzky-Golay smoothing, the baseline correction method, the order of preprocessing steps, spectral regions, and the number of latent variables. The evaluation method for the model was considered as a factor subject to noise. The optimal combination of levels was determined through the signal-to-noise ratio response table employing Taguchi analysis. The effectiveness of this approach was validated through two cases, involving different cultivation scales, different Raman spectrometers, and different analytical components. The results consistently demonstrated that the proposed approach closely approximated the global optimum, regardless of data set size, predictive components, or the brand of Raman spectrometer. The performance of models recommended by the DoE strategy consistently surpassed those built using raw data, underscoring the reliability of models generated through this approach. When compared to exhaustive all-combination experiments, the DoE approach significantly reduces calculation times, making it highly practical for the implementation of Raman spectroscopy in bioprocess monitoring.

Application of hyperspectral imaging and colorimetric sensor array coupled with multivariate analysis for quality detection during salted duck eggs processing

AbstractIn order to realize the evaluation of salted duck eggs quality, this study constructed salts‐sensitive colorimetric sensor array (CSA) based on silver nanoprisms (AgNPRs) etching principle and utilized hyperspectral imaging (HSI) system to capture the reflectance spectral of CSA and section of salted duck eggs. To minimize the noise in spectral signals, three preprocessing methods including Savitzky–Golay Smoothing (SG), standard normal variate (SNV) transformation, and multiplicative scatter correction were used. The competitive adaptive reweighted sampling and uninformative variables elimination were used to select the optimal wavelengths, and then partial least squares regression was used to predict moisture, lipid, and salt content (SC). Results demonstrated coefficients of determination of 0.8200, 0.8969, 0.8709, and 0.9239 for protein moisture, yolk moisture, yolk lipid, and protein SC. Visualization distribution maps successfully display the distribution of moisture and lipid content during salting periods. The results indicated that the CSA based on AgNPRs etching principle can be an effective tool in quantitative measurement of salt and HSI combining CSA can be applied to evaluate the quality of salted duck eggs comprehensively. The study offers a rapid detection method for the production and processing of salted duck eggs in the industry.Practical applicationA novel salts‐sensitive CSA was constructed to evaluate quality of salted duck eggs based on AgNPRs etching. This study designed and developed a portable sample collection device using 3D printing technology to acquire the information of samples and CSA at the same time. This study combined with HSI system and colorimetric sensor technology to evaluate the quality of salted duck eggs during the salting process.

Estimation of Relative Chlorophyll Content in Lettuce (Lactuca sativa L.) Leaves under Cadmium Stress Using Visible—Near-Infrared Reflectance and Machine-Learning Models

Chlorophyll content is a crucial assessment parameter in the growth monitoring of lettuce, particularly in cases when it is affected by disease. Accurate estimation of chlorophyll content is beneficial for early detection and prevention of diseases and holds significant importance in practical production. To construct a model for estimating the chlorophyll content in lettuce leaves under cadmium stress, this study utilized lettuce as the experimental material. The visible–near-infrared reflectance spectra of lettuce leaves, as well as the relative chlorophyll content of the leaves, were detected and analyzed under different concentrations of cadmium stress. Subsequently, an inversion model for estimating the relative chlorophyll content in lettuce leaves was established. First, to determine the optimal spectral preprocessing method, eight techniques are utilized: Savitzky–Golay smoothing (SG), multiplicative scatter correction (MSC), standard normal variable transformation (SNV), mean normalization (MN), baseline offset (B), detrending (D), gap derivatives—first derivative (FD), and gap derivatives—second derivative (SD). These methods are used to preprocess the spectra and establish a partial least squares regression (PLSR) monitoring model. The optimal spectral preprocessing method is then selected. Next, the feature bands are extracted from the preprocessed spectral data using the correlation coefficient method. Finally, the selected feature bands will be combined with support vector regression (SVR) to establish a chlorophyll content estimation model using a training-to-testing set ratio of 4:1. The results showed that the PLSR model established after preprocessing with detrending (D) had the highest accuracy, with the coefficient of determination (Rv2) and root mean squared error (RMSEv) values of 0.87 and 1.16, respectively. The feature bands selected by the correlation coefficient method were used to establish SVR models for estimating the chlorophyll content of lettuce leaves under cadmium stress, with the highest accuracy being achieved by the genetic algorithm (GA)–SVR model. It can be seen that near-infrared spectroscopy technology provides a scientific basis for rapid, nondestructive, and accurate detection of lettuce diseases and stress.

Artificial neural network and convolutional neural network for prediction of dental caries

Dental caries has high prevalence among kids and adults thus it has become one of the global health concerns. The current modern dentistry focused on the preventives measures to reduce the number of dental caries cases. The employment of machine learning coupled with UV spectroscopy plays a crucial role to detect the early stage of caries. Artificial neural network with hyperparameter tuning was employed to train spectral data for the classification based on the International Caries Detection and Assesment System (ICDAS). Spectra preprocessing namely mean center (MC), autoscale (AS) and Savitzky Golay smoothing (SG) were applied on the data for spectra correction. The best performance of ANN model obtained has accuracy of 0.85 with precision of 1.00. Convolutional neural network (CNN) combined with Savitzky Golay smoothing performed on the spectral data has accuracy, precision, sensitivity and specificity for validation data of 1.00 respectively. The result obtained shows that the application of ANN and CNN capable to produce robust model to be used as an early screening of dental caries.

Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy

Widespread adoption of machine-picked cotton in China, the impurity content of seed cotton has increased significantly. This impurity content holds direct implications for the valuation of seed cotton and exerts a consequential influence on the ensuing quality of processed lint and textiles. Presently, the primary approach for assessing impurity content in seed cotton primarily depends on semi-automated testing instruments, exhibiting suboptimal detection efficiency and not well-suited for the impurity detection requirements during the purchase of seed cotton. To address this challenge, this study introduces a seed cotton near-infrared spectral (NIRS) data acquisition system, facilitating the rapid collection of seed cotton spectral data. Three pretreatment algorithms, namely SG (Savitzky-Golay convolutional smoothing), SNV (Standard Normal Variate Transformation), and Normalization, were applied to preprocess the seed cotton spectral data. Cotton-Net, a one-dimensional convolutional neural network aligned with the distinctive characteristics of the seed cotton spectral data, was developed in order to improve the prediction accuracy of seed cotton impurity content. Ablation experiments were performed, utilizing SELU, ReLU, and Sigmoid functions as activation functions. The experimental outcomes revealed that after normalization, employing SELU as the activation function led to the optimal performance of Cotton-Net, displaying a correlation coefficient of 0.9063 and an RMSE (Root Mean Square Error) of 0.0546. In the context of machine learning modeling, the LSSVM model, developed after Normalization and Random Frog algorithm processing, demonstrated superior performance, achieving a correlation coefficient of 0.8662 and an RMSE of 0.0622. In comparison, the correlation coefficient of Cotton-Net increased by 4.01%. This approach holds significant potential to underpin the subsequent development of rapid detection instruments targeting seed cotton impurities.

LIBS combined with SG-SPXY spectral data pre-processing for cement raw meal composition analysis.

Rapid testing of cement raw meal plays a crucial role in the cement production process, so there is an urgent need for a fast and accurate testing method. In this paper, a method based on the Savitzky-Golay (SG) smoothing and sample set partitioning based on joint x-y distance (SPXY) spectral data pre-processing is proposed to improve the accuracy of the laser-induced breakdown spectroscopy (LIBS) technique for quantitative analysis of cement raw meal components. Firstly, the spectral data is denoised by SG smoothing, which effectively reduces the noise and baseline variations in the spectra. Then, the denoised data is divided into sample sets by combining the SPXY sample division method, which improves the efficiency of data analysis. Finally, the delineated data set is modeled for quantitative analysis by a back-propagation (BP) neural network. Compared to the modeling effect of the four oxide contents of CaO, S i O 2, A l 2 O 3, and F e 2 O 3 in the Hold-Out method, the correlation coefficient (R) was improved by 26%, 10%, 17%, and 4%, respectively. The root mean square error (RMSE) was reduced by 47%, 33%, 43%, and 21%, respectively. The mean absolute percentage error (MAPE) was reduced by 63%, 60%, 36%, and 51%, respectively. The results show that there is a significant improvement in the model effect, which can effectively improve the accuracy of quantitative analysis of cement raw meal composition by LIBS. This is of great significance for the real-time detection of cement raw meal composition analysis.

Determination of soluble solids content of multiple varieties of tomatoes by full transmission visible-near infrared spectroscopy.

Soluble solids content (SSC) is a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming. To enhance accuracy and efficiency in SSC assessment, this study employs full transmission visible and near-infrared (Vis-NIR) spectroscopy and multi-point spectral data collection techniques to quantitatively analyze SSC in two tomato varieties ('Provence' and 'Jingcai No.8' tomatoes). Preprocessing of the multi-point spectra is carried out using a weighted averaging approach, aimed at noise reduction, signal-to-noise ratio improvement, and overall data quality enhancement. Taking into account the potential influence of various detection orientations and preprocessing methods on model outcomes, we investigate the combination of partial least squares regression (PLSR) with two orientations (O1 and O2) and two preprocessing techniques (Savitzky-Golay smoothing (SG) and Standard Normal Variate transformation (SNV)) in the development of SSC prediction models. The model achieved the best results in the O2 orientation and SNV pretreatment as follows: 'Provence' tomato (Rp = 0.81, RMSEP = 0.69°Brix) and 'Jingcai No.8' tomatoes (Rp = 0.84, RMSEP = 0.64°Brix). To further optimize the model, characteristic wavelength selection is introduced through Least Angle Regression (LARS) with L1 and L2 regularization. Notably, when λ=0.004, LARS-L1 produces superior results ('Provence' tomato: Rp = 0.95, RMSEP = 0.35°Brix; 'Jingcai No.8' tomato: Rp = 0.96, RMSEP = 0.33°Brix). This study underscores the effectiveness of full transmission Vis-NIR spectroscopy in predicting SSC in different tomato varieties, offering a viable method for accurate and swift SSC assessment in tomatoes.

SHAP values accurately explain the difference in modeling accuracy of convolution neural network between soil full-spectrum and feature-spectrum

Acquiring soil nutrient content quickly and accurately through remote sensing is the key to advance precision agriculture. The development of deep learning has provided new technical means for soil hyperspectral modeling. However, the problem of poor interpretability of deep learning models limits its development. Although SHapley Additive exPlanations (SHAP) values based on game theory have been successfully applied to the interpretation of deep learning soil spectral modeling, whether they can accurately explain the differences in deep learning model accuracy remains to be verified. Based on this, we explored whether SHAP values can accurately explain the differences in convolutional neural network (CNN) modeling accuracy. We collected soil samples from agricultural land in the Liangshui River Basin in the southern mountainous and hilly areas of China, and measured the soil total nitrogen (STN) content and soil spectral data in the laboratory. We compared the effects of full-spectrum and feature-spectrum on the accuracy of deep learning models, and obtained the contribution of wavelengths in the CNN modeling process by calculating SHAP values. The results showed that combining different spectral pre-processing methods can play their respective advantages and help improve modeling accuracy. Among them, the CNN model obtained the highest prediction accuracy under the first-derivative Savitzky-Golay smoothing combination standard normal variate (SG1-SNV) spectral pre-processing in full-spectrum modeling. Compared with the feature-spectrum selected for modeling by Mutual information (MI) and competitive adaptive reweighted sampling (CARS), the CNN model achieved higher accuracy in most pre-processed spectra in full-spectrum modeling, and SHAP values accurately explained this reason. This is because the contribution is usually higher at most wavelengths with a high correlation with STN content. The feature-spectrum selected by CARS is more widely distributed but lacks continuity, and some wavelengths with high correlation and high contribution will also be missed. Meanwhile, some wavelengths with low correlation also have high contributions, which are usually not involved in the feature spectrum modeling of MI, thus affecting the modeling accuracy. Therefore, the deep learning model is more suitable for full-spectrum modeling due to its strong feature extraction and self-learning capabilities, and SHAP can obtain the wavelength contribution of the CNN model in soil spectral modeling, and then explain the differences in modeling accuracy. This study further proves the interpretability of deep learning, provides an important basis for the application of deep learning in soil hyperspectral modeling.

Non-destructive estimation of needle leaf chlorophyll and water contents in Chinese fir seedlings based on hyperspectral reflectance spectra.

Chinese fir is the most important native softwood tree in China and has significant economic and ecological value. Accurate assessment of the growth status is critical for both seedling cultivation and germplasm evaluation of this commercially significant tree. Needle leaf chlorophyll content (LCC) and needle leaf water content (LWC), which are determinants of plant health and photosynthetic efficiency, are important indicators of the growth status in plants. In this study, for the first time, the LCC and LWC of Chinese fir seedlings were estimated based on hyperspectral reflectance spectra and machine learning algorithms. A line-scan hyperspectral imaging system with a spectral range of 870 to 1,720 nm was used to capture hyperspectral images of seedlings with varying LCC and LWC. The spectral data of the canopy area of the seedlings were extracted and preprocessed using the Savitzky-Golay smoothing (SG) algorithm. Subsequently, the Successive Projection Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) methods were employed to extract the most informative wavelengths. Moreover, SVM, PLSR and ANNs were utilized to construct models that predict LCC and LWC based on effective wavelengths. The results indicated that the CARS-ANNs were the best for predicting LCC, with R²C = 0.932, RSMEC = 0.224, and R²P = 0.969, RSMEP = 0.157. Similarly, the SPA-ANNs model exhibited the best prediction performance for LWC, with R²C = 0.952, RSMEC = 0.049, and R²P = 0.948, RSMEP = 0.051. In conclusion, the present study highlights the significant potential of combining hyperspectral imaging (HSI) with machine learning algorithms as a rapid, non-destructive, and highly accurate method for estimating LCC and LWC in Chinese fir.

Integrating near-infrared hyperspectral imaging with machine learning and feature selection: Detecting adulteration of extra-virgin olive oil with lower-grade olive oils and hazelnut oil

Detecting adulteration in extra virgin olive oil (EVOO) is particularly challenging with oils of similar chemical composition. This study applies near-infrared hyperspectral imaging (NIR-HSI) and machine learning (ML) to detect EVOO adulteration with hazelnut, refined olive, and olive pomace oils at various concentrations (1%, 5%, 10%, 20%, 40%, and 100% m/m). Savitzky-Golay filtering, first and second derivatives, multiplicative scatter correction (MSC), standard normal variate (SNV), and their combinations were used to preprocess the spectral data, with Principal Component Analysis (PCA) reducing dimensionality. Classification was performed using Partial Least Squares-Discriminant Analysis (PLS-DA) and ML algorithms, including k-Nearest Neighbors (k-NN), Naïve Bayes, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). PLS-DA, k-NN, RF, SVM, NB, and ANN models achieved accuracy rates of 97.0–99.0%, 96.2–100%, 96.5–100%, 98.6–99.5%, 93.9–99.7%, and 99.2–100%, respectively, in discriminating between pure EVOO, adulterants, and adulterated oils. PLS-DA, RF, SVM, and ANN significantly outperformed Naïve Bayes (p < 0.05) in binary classification, with Matthews correlation coefficient (MCC) values exceeding 0.90. All the binary classifiers except Naïve Bayes, when coupled with SNV/MSC, Savitzky-Golay smoothing and derivatives, consistently achieved perfect scores (1.0) for accuracy, sensitivity, specificity, F1 score, precision, and MCC in distinguishing pure EVOO from adulterated oils. No significant differences (p > 0.05) in model performance were found between those using full spectra and those based on key variable selection. However, PLS-DA and ANN significantly outperformed k-NN, RF, and SVM (p < 0.05), with MCC values ranging from 0.95 to 1.00, indicating superior classification performance. These findings demonstrate that combining NIR-HSI with machine learning, along with key variable selection, potentially offers an effective, non-destructive solution for detecting adulteration in EVOO and combating fraud in the olive oil industry.

Breast Cancer Diagnosis Using Virtualization and Extreme Learning Algorithm Based on Deep Feed Forward Networks

One of the leading causes of death for women worldwide is breast cancer. Early detection and prompt treatment can reduce the risk of breast cancer-related death. Cloud computing and machine learning are crucial for disease diagnosis today, but they are especially important for those who live in distant places with poor access to healthcare. While machine learning-based diagnosis tools act as primary readers and aid radiologists in correctly diagnosing diseases, cloud-based technology can also assist remote diagnostics and telemedicine services. The promise of techniques based on Artificial Neural Networks (ANN) for sickness diagnosis has attracted the attention of several re-searchers. The 4 methods for the proposed research include preprocessing, feature extraction, and classification. A Smart Window Vestige Deletion (SWVD) technique is initially suggested for preprocessing. It consists of Savitzky-Golay (S-G) smoothing, updated 2-stage filtering, and adaptive time window division. This technique separates each channel into multiple time periods by adaptively pre-analyzing its specificity. On each window, an altered 2-stage filtering process is then used to retrieve some tumor information. After applying S-G smoothing and integrating the broken time sequences, the process is complete. In order to deliver effective feature extraction, the Deep Residual based Multiclass for architecture (DRMFA) is used. In histological photos, identify characteristics at the cellular and tissue levels in both tiny and large size patches. Finally, a fresh customized strategy that combines a better crow forage-ELM. Deep learning and the Extreme Learning Machine (ELM) are concepts that have been developed (ACF-ELM). When it comes to diagnosing ailments, the cloud-based ELM performs similarly to certain cutting-edge technology. The cloud-based ELM approach beats alternative solutions, according to the DDSM and INbreast dataset results. Significant experimental results show that the accuracy for data inputs is 0.9845, the precision is 0.96, the recall is 0.94, and the F1 score is 0.95.

Measurement of Ammonia Leakage by TDLAS in Mid-Infrared Combined with an EMD-SG Filter Method

Ammonia (NH3) has been used as the main refrigerant in various types of cold storages, exceeding 90% of cooling applications. However, NH3 leakage occurs frequently due to damage of the refrigeration compressor, resulting in environmental pollution and intoxication accidents. Therefore, reliable and highly sensitive NH3 detection is a critical requirement in cold storage. In this paper, tunable diode laser absorption spectroscopy (TDLAS) is used to measure NH3 leakage, where a new denoising method combining empirical mode decomposition with the Savitzky-Golay smoothing algorithm (EMD-SG) is proposed to improve the signal-to-noise ratio (SNR) of absorbance signals. The results show that a 5.5 times improvement of SNR and 86% reduction of the linear fitting error in calibration is obtained; furthermore, this system achieves a detection limit of 0.2 ppm at an average time of 69 s by Allan variance analysis. Hence, it can be concluded that the EMD-SG algorithm can effectively improve the performance of TDLAS systems to meet the requirements for NH3 leakage detection in cold storages.

Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms.

Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky-Golay smoothing (SG), and wavelet transform methods (WT) were applied to preprocess Raman spectra. A principal component analysis (PCA) was used to extract spectral features, and the PCA score plots clustered four toxins with two other proteins. The k-means clustering results show that the spectra processed with MSC and MSC-SG methods have the best classification performance. Then, the two data types were classified using partial least squares discriminant analysis (PLS-DA) with an accuracy of 100%. The prediction results of the PCA and PLS-DA and the partial least squares regression model (PLSR) perform well for the fingerprint region spectra. The PLSR model demonstrates excellent classification and regression ability (accuracy = 100%, Rcv = 0.776). Four toxins were correctly classified with interference from two proteins. Classification models based on spectral feature extraction were established. This strategy shows excellent potential in toxin detection and public health protection. These models provide alternative paths for the development of rapid detection devices.

Near-Infrared Spectroscopy with Supervised Machine Learning as a Screening Tool for Neutropenia.

The use of non-invasive tools in conjunction with artificial intelligence (AI) to detect diseases has the potential to revolutionize healthcare. Near-infrared spectroscopy (NIR) is a technology that can be used to analyze biological samples in a non-invasive manner. This study evaluated the use of NIR spectroscopy in the fingertip to detect neutropenia in solid-tumor oncologic patients. A total of 75 patients were enrolled in the study. Fingertip NIR spectra and complete blood counts were collected from each patient. The NIR spectra were pre-processed using Savitzky-Golay smoothing and outlier detection. The pre-processed data were split into training/validation and test sets using the Kennard-Stone method. A toolbox of supervised machine learning classification algorithms was applied to the training/validation set using a stratified 5-fold cross-validation regimen. The algorithms included linear discriminant analysis (LDA), logistic regression (LR), random forest (RF), multilayer perceptron (MLP), and support vector machines (SVMs). The SVM model performed best in the validation step, with 85% sensitivity, 89% negative predictive value (NPV), and 64% accuracy. The SVM model showed 67% sensitivity, 82% NPV, and 57% accuracy on the test set. These results suggest that NIR spectroscopy in the fingertip, combined with machine learning methods, can be used to detect neutropenia in solid-tumor oncology patients in a non-invasive and timely manner. This approach could help reduce exposure to invasive tests and prevent neutropenic patients from inadvertently undergoing chemotherapy.

Prediction of soluble solid content in Nanfeng mandarin by combining hyperspectral imaging and effective wavelength selection

Soluble solid content (SSC) is among primary evaluation indicators of fruit quality and a key factor influencing consumer purchasing decisions. The research utilized hyperspectral imaging (380–1030 nm) to forecast SSC in Nanfeng mandarin. After a series of preprocessing methods, partial least squares regression (PLSR) and least squares support vector machine (LSSVM) were adopted to build prediction models. Combining multiplicative scatter correction and Savitzky-Golay smoothing was more effective compared to other preprocessing methods. Effective wavelengths (EWs) were selected by using bootstrapping soft shrinkage (BOSS), competitive adaptive reweighted sampling (CARS), iteratively retaining informative variables (IRIV) and their combinations. The BOSS-CARS-PLSR model performs optimally in prediction with the Rp2, RMSEP and RPD being 0.9376, 0.3986 and 4.0542, respectively. Additionally, the spatial distribution of the SSC in Nanfeng mandarin was visualized using the optimal model. Results show that combining hyperspectral imaging and EWs selection offers a rapid and intuitive approach that can non-destructively evaluate internal quality of Nanfeng mandarin.

Online Determination on the Properties of Naphtha as the Ethylene Feedstock Using Near-Infrared Spectroscopy

Providing real-time information on the properties of naphtha as the ethylene feedstock within the minimal time is significant for improvement of the process simulation, control, and real-time optimization. To develop models predicting naphtha properties for different pre-processing methods, an online full transmittance near-infrared (NIR) spectrum measurement system has been used along with the principal component regression and partial least squares (PLS) methods. The results show that the Savitzky-Golay smoothing combined with the first-derivative pre-processing provides the best denoising effect compared to other methods. The predicted relative errors of the NIR models developed by PLS, especially for the cutting temperature points of the test set, basically make 1‒5% indicating it can be used to create good NIR prediction models for the on-line determination of naphtha properties.

Exploring the optimal model for assessing SOC and TN in Zanthoxylum bungeanum forest on the Loess Plateau using VNIR spectroscopy

Zanthoxylum bungeanum forest is an important economic forest on the Loess Plateau. The rapid assessment of its soil organic carbon (SOC) and total nitrogen (TN) concentrations is critical for the evaluation of soil quality. Soil samples of different forest ages and soil depths were collected for laboratory analyses and spectral measurements, and the visible and near-infrared reflectance (VNIR) spectral data were processed by mathematical transformations, such as first-order derivative (FD) and second-order derivative, multiple scattering correction, and logarithm of the reciprocal. The importance bands of SOC and TN were screened on the basis of competitive adaptive reweighted sampling. In addition, partial least squares regression and support vector machine regression models were constructed, and the applicability of SOC and TN to the models were compared. Results showed that forest age and soil depth remarkably affect SOC and TN concentrations. Among the calibration and validation models, the combination of the SVMR model with Savitzky–Golay smoothing and FD estimated SOC and TN with the highest accuracy, and the validation accuracy of the surface SOC and TN was higher than that of the bottom depths. Meanwhile, the importance bands involved in the modeling were mainly distributed in 400–600 nm and 1000–2400 nm. The combination of the FD transformation and the SVMR model is suitable for determining SOC and TN concentrations in Z. bungeanum forests, and the model has great potential for the quantitative evaluation of soil quality in the Loess Plateau.

Rapid and non-destructive detection of ponceau 4R red colored pork

The characteristic colour of pork desired by consumers is a widespread phenomenon on the Ghanaian market that has led to some suspected adulteration practices. Currently available methods for monitoring pork quality are time consuming but above all, destructive (destroys the integrity of meat). This study aimed to develop rapid models that can be used to detect, classify and predict the presence of ponceau 4R in fresh pork in the Kumasi metropolis of Ghana using near-infrared spectroscopy together with chemometrics. Fresh pork samples, 120 obtained from the markets and 120 adulterated artificially in the laboratory, were subjected to near-infrared measurements. The spectra obtained were evaluated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Partial Least Square Regression (PLSR). PCA and LDA showed that scanning the skin of the pork and pretreating the spectra with Savitzky-Golay smoothing sufficed for further chemometric analysis. The classification models built using LDA showed similarities between samples obtained from the markets and the artificially adulterated samples, indicating the presence of colour adulterant. The models also revealed the importance of processing time in making the adulterated meat more appealing to consumers. PLSR, however, yielded poor results for predicting colour and adulterant concentration. In effect, PCA and LDA methods proved to be better alternatives for the detection of colored pork adulteration and can be adopted for quality control applications together with near infrared spectroscopy.