Visible Near-infrared Research Articles

Integrating transfer learning and spectroscopy for enhanced pork spoilage assessment using correlation analysis

Accurate Total Viable Count (TVC) detection is vital for food quality monitoring. In this study, we investigated the feasibility of using visible near-infrared (VNIR) spectroscopy (400–1000 nm) combined with transfer learning (TL) to track the chemical spoilage of pork. The base models developed using the full band for pork TVC, total volatile basic nitrogen, pH, and color showed predictability; the correlation coefficient of prediction set (RP) for all models ranged from 0.821 to 0.916; and the root mean square error of prediction set (RMSEP) of the TVC model was 0.617 (lg CFU/g). A correlation analysis of the different indexes of pork was carried out to optimize the TVC calibration model. Different TL methods for TVC optimization were designed. The results showed that multiple correlation chain stacking-partial least squares performed best with RP, RMSEP, and the relative percent deviation of 0.947, 0.425 lg CFU/g, and 2.355, respectively, the RMSEP of TVC was reduced by 31.12 % as compared to the base model. This study demonstrated the possibility of combining the VNIR spectroscopy system with TL to monitor the degree of meat's chemical spoilage.

Combining hyperspectral imaging technology and visible-near infrared spectroscopy with a data fusion strategy for the detection of soluble solids content in apples

Soluble solids content (SSC) is an important indicator for evaluating apple quality. This study aimed to assess two spectral techniques (hyperspectral imaging (HSI) and visible-near infrared (Vis-NIR)) combined with low-level and mid-level fusion strategies (LLF and MLF) for the detection of SSC in apples. Firstly, baseline correction (BC), detrending (DT), multiplicative scatter correction (MSC), savitzky-golay (SG), and standard normal variables (SNV) were used for the preprocessing of spectral data. Secondly, genetic algorithm (GA), competitive adaptive reweighted sampling (CARS), and their combinations were used for feature variable extraction. Finally, models were developed using partial least squares regression (PLSR) for spectral data. The results showed that Vis-NIR has an advantage over HSI in predicting apple SSC if only a single spectral technique was considered. The data fusion strategy showed better performance in predicting SSC metrics compared to individual spectral data. Among them, the LLF strategy showed the best performance in predicting SSC, with an Rp2 of 0.927, an RMSEP of 0.529 °Brix, and an Akike information criterion (AIC) of 332.96. In addition, an optimal model based on HSI data was used to achieve visual maps of SSC. It was demonstrated that the fusion of HSI and Vis-NIR data provided a promising method for detecting the SSC in apples.

Early detection of beef-quality indicators using hyperspectral imaging combined with pixel-based segmentation method corresponding to fat and protein region

This study investigated the utilization of hyperspectral imaging (HSI) in conjunction with pixel-based segmentation to predict the thiobarbituric acid-reactive substances (TBARS) and volatile basic nitrogen (VBN) content in beef. Hyperspectral images were acquired in the visible near-infrared (VIS-NIR) and shortwave infrared (SWIR) ranges to examine temporal alterations in the fat and protein regions. A partial least squares discriminant analysis (PLS-DA) model was employed to segment fat and protein pixels, followed by a partial least squares regression (PLSR) model to predict the TBARS and VBN content from the segmented spectra. The SWIR range yielded the most accurate predictions, with an Rp2 of 0.899 for the early freshness indicators. Utilizing hyperspectral information from individual fat and protein pixels, rather than modeling the entire beef image, resulted in enhanced prediction accuracy for Rp2 of TBARS (0.814–0.899) and VBN (0.394–0.532) in the early stages of storage. These findings elucidate the potential of HSI with pixel-based segmentation as a nondestructive and real-time methodology for precise monitoring of beef freshness.

Nondestructive Detection of Litchi Stem Borers Using Multi-Sensor Data Fusion

To enhance lychee quality assessment and address inconsistencies in post-harvest pest detection, this study presents a multi-source fusion approach combining hyperspectral imaging, X-ray imaging, and visible/near-infrared (Vis/NIR) spectroscopy. Traditional single-sensor methods are limited in detecting pest damage, particularly in lychees with complex skins, as they often fail to capture both external and internal fruit characteristics. By integrating multiple sensors, our approach overcomes these limitations, offering a more accurate and robust detection system. Significant differences were observed between pest-free and infested lychees. Pest-free lychees exhibited higher hardness, soluble sugars (11% higher in flesh, 7% higher in peel), vitamin C (50% higher in flesh, 2% higher in peel), polyphenols, anthocyanins, and ORAC values (26%, 9%, and 14% higher, respectively). The Vis/NIR data processed with SG+SNV+CARS yielded a partial least squares regression (PLSR) model with an R2 of 0.82, an RMSE of 0.18, and accuracy of 89.22%. The hyperspectral model, using SG+MSC+SPA, achieved an R2 of 0.69, an RMSE of 0.23, and 81.74% accuracy, while the X-ray method with support vector regression (SVR) reached an R2 of 0.69, an RMSE of 0.22, and 76.25% accuracy. Through feature-level fusion, Recursive Feature Elimination with Cross-Validation (RFECV), and dimensionality reduction using PCA, we optimized hyperparameters and developed a Random Forest model. This model achieved 92.39% accuracy in pest detection, outperforming the individual methods by 3.17%, 10.25%, and 16.14%, respectively. The multi-source fusion approach also improved the overall accuracy by 4.79%, highlighting the critical role of sensor fusion in enhancing pest detection and supporting the development of automated non-destructive systems for lychee stem borer detection.

Prediction of soil organic carbon and total nitrogen affected by mine using Vis–NIR spectroscopy coupled with machine learning algorithms in calcareous soils

The utilization of visible-near infrared (Vis–NIR) spectroscopy presents a nondestructive, fast, reliable and cost-effective approach to predicting total nitrogen (TN) and organic carbon (OC) levels. This study employed a combination of Vis–NIR spectroscopy, partial least-squares regression (PLSR), and support vector machine (SVM) models to investigate the effects of mining on TN and OC stocks in both the topsoil (0–10 cm) and subsoil (10–40 cm). 105 soil samples were collected from agricultural areas near an iron mine, polluted, moderately-polluted, and non-polluted sites. Results indicated that soils at the non-polluted site had the highest of soil OC stocks (7.5 kg m–2) and total nitrogen (2.5 kg m–2), followed by the moderately-polluted site. Furthermore, it was observed that soils from the polluted site displayed the highest spectral reflectance. The spectral bands in the range of 500–700 nm showed the strongest correlation with soil organic carbon content. Notably, the SVM method utilizing Vis–NIR spectroscopy provided superior predictions for both subsoil and topsoil organic carbon and total nitrogen compared to the PLSR methods. Additionally, SVM demonstrated better performance in predicting topsoil soil organic carbon (R2 = 0.87, RMSE = 0.13%, and RPD = 2.8) and total nitrogen (R2 = 0.91, RMSE = 0.13%, and RPD = 2.4) compared to the subsoil, owing to the larger OM content in the topsoils.

Detection of soluble solids content in tomatoes using full transmission Vis-NIR spectroscopy and combinatorial algorithms.

Soluble solids content (SSC) is an important indicator for evaluating tomato flavor, and general physical and chemical methods are time-consuming and destructive. This study utilized full transmittance visible and near infrared (Vis-NIR) spectroscopy for multi-posed data acquisition of tomatoes in different orientations. The role of two directions (Z1 and Z2) and four preprocessing techniques, as well as three wavelength selection methods in the exploitation of SSC regression models was investigated. After using the Outlier elimination method, the spectra acquired in the Z2 direction and the raw spectral data processed by preprocessing methods gave the best result by the PLSR model (Rp = 0.877, RMSEP = 0.417 %). Compared to the model built using the full 2048 spectral wavelengths, the prediction accuracy using 20 wavelengths obtained by a combination wavelength selection: backward variable selection - partial least squares and simulated annealing (BVS-PLS and SA) was further improved (Rp = 0.912, RMSEP = 0.354 %). The findings of this research demonstrate the efficacy of full-transmission visible-near infrared (Vis-NIR) spectroscopy in forecasting SSC of tomatoes, and most importantly, the combination of the packing method in wavelength selection with an intelligent optimization algorithm provides a viable idea for accurately and rapidly assessing the SSC of tomatoes.

Calibration of MAJIS (Moons and Jupiter Imaging Spectrometer). IV. Radiometric calibration (invited)

The MAJIS (Moons and Jupiter Imaging Spectrometer) instrument is an imaging spectrometer on-board the JUICE (JUpiter ICy moons Explorer) spacecraft. MAJIS covers the spectral range from 0.5 to 5.54 μm with two channels [visible–near infrared (VISNIR) and IR]. A comprehensive campaign of on-ground MAJIS calibration was conducted in August and September 2021 in the IAS (Institut d’Astrophysique Spatiale, CNRS/Université Paris-Saclay) facilities. In this article, we present the results relevant for the radiometric calibration of MAJIS. Due to the specific characteristics of the MAJIS detectors (H1RG from Teledyne), an extensive detector characterization campaign was implemented for both the VISNIR and IR detectors before integration so as to validate readout procedures providing precision and accuracy. The characterization also provided critical information on linearity and operability as a function of the integration time and operating temperature. The radiometric calibration of the integrated MAJIS instrument focused on the determination of the instrument transfer function in terms of DN output per unit of radiance for each MAJIS data element as a function of its position in the field of view of MAJIS and its central wavelength. The radiometric calibration of the VISNIR channel required a specific procedure due to stray light at short wavelengths. Observations of an internal calibration source during calibration and after launch (April 14, 2023) showed that there were minor changes in both the VISNIR and IR channels. The instrument transfer functions to be used in flight have been updated on this basis.

Enhancing soil nitrogen measurement via visible-near infrared spectroscopy: Integrating soil particle size distribution with long short-term memory models

Good quality of soil nitrogen data, which is essential for the advancement of both enhanced agricultural management and ecological environment, traditionally depends on labor intensive chemical procedures. Visible near-infrared (Vis-NIR) spectroscopy, acknowledged for its efficiency, environmental compatibility and rapidity, merges as a promising alternative. However, the effectiveness of Vis-NIR measurement models are significantly compromised by soil particle size distribution (PSD), presenting a substantial challenge in improving the measurement accuracy and reliability. Here an innovative deep learning methodology that integrates PSD with Vis-NIR spectroscopy was proposed for the measurement of nitrogen content in soil samples. By leveraging the LUCAS dataset, different strategies for integrating PSD with Vis-NIR spectral data in deep learning models were explored, revealing that our proposed InSGraL framework, which incorporated mixed features of PSD and spectra as LSTM inputs achieves superior performance. Compared to models utilizing solely Vis-NIR data, InSGraL exhibits a 39.47 % reduction in RMSE and a 42.55 % decrease in MAE, and demonstrates robust performance across various land cover types, achieving an R2 of 0.94 on grassland samples. Moreover, Shapley Additive exPlanations (SHAP) analysis revealed that incorporating PSD modifies the spectral input importance distribution, effectively mitigating spectral interference from particle size while highlighting critical wavelengths previously obscured. This study provides an innovative modeling strategy to mitigate the influence of PSD by integrating it within deep learning framework using Vis-NIR, contributing a deeper understanding of the relationship between PSD and Vis-NIR spectra for the measurement of nitrogen content and offering an effective means to attain soil nitrogen data.

Space and ground based spectroscopic studies and mineral chemistry of rare earth element bearing peralkaline rocks from Siwana Ring Complex, Rajasthan, India

The Neoproterozoic Siwana Ring Complex (SRC) in Barmer district, Rajasthan, India, contains a promising concentration of rare earth elements (REEs). This study uses PRISMA hyperspectral data to map potential high-REE locations using the Spectral Angle Mapper (SAM) classifier. Also, laboratory-based spectroscopy is used to analyse rock samples of peralkaline granite and rhyolite, using spectroradiometer in 400–2500 nm and Fourier Transform Infrared (FTIR) Spectroscopy in 400–4000 cm⁻1 regions. Diagnostic absorption features in the Visible Near Infrared (VNIR) and Short-Wave Infrared (SWIR) regions indicate the presence of REEs, alongside pyroxenes, amphiboles, and phyllosilicates. The data from the FTIR indicates the presence of minerals possessing Si-O, P-O and O-H bonds. Mineral chemistry and geochemistry confirm these findings, indicating the occurrence of REE-bearing minerals such as monazite, REE silicates, and apatite. Ce is found to be the most abundant REE. However, only absorption features related to Nd and Er or Ho are observed which means that these elements are indicators of high concentration of total REE in the samples. The map generated from the PRISMA data aligns well with geochemical data, proving the value of hyperspectral remote sensing in early exploration. This study also highlights laboratory spectroscopy as a valuable complementary tool in mineral exploration. This study provides spectral information for reference for future use on REE bearing peralkaline rocks.

Wheat grain classification using hyperspectral imaging: Concatenating Vis-NIR and SWIR Data for single and bulk grains

Wheat variety identification is an essential component of the official inspection of wheat grains to determine their quality and trade value. Traditionally, wheat class identification requires deep knowledge about the appearance of plants and kernels, their history and distribution. Therefore, there is an urgent need for an automatic technology to standardize the nomenclature of wheat varieties to enable both growers and producers to identify their grains practically. The present study investigated the feasibility of Visible-Near Infrared (Vis-NIR) and Short-Wave Infrared (SWIR) spectral imaging to identify wheat classes. Both sides of the wheat kernel as single kernel measurements and bulk grains were used to acquire hyperspectral data and vertical and horizontal data concatenation was implemented to explore performance differences. In addition, the classification of bulk kernels from single kernel data was investigated. Spectral data was pre-treated by standard normal variate (SNV), Savitzky-Golay first (SG-1) and second derivatives (SG-2) and linear discriminant analysis (LDA), support vector machine (SVM) and artificial neural network (ANN) were applied as the classification algorithms. Furthermore, feature selection was utilised using the minimum redundancy maximum relevance (mRMR) algorithm to investigate the performance difference between data concatenation and feature selection. The results showed that ventral (up) side data showed better classification performances than reverse (down) side data, while Vis-NIR data achieved higher classification accuracies than SWIR data. However, the best classification performance for single kernels was obtained by LDA-SNV using up and down data in the Vis-NIR and SWIR regions vertically and horizontally concatenated with an accuracy of 93.72% for 10-fold cross-validation and 94.93% for test sets. Models based on a hundred features did not achieve the accuracy of models based on concatenated data. Moreover, classification performances of bulk samples were higher than single kernels, which achieved 100% accuracy for both cross-validation and test sets. The study demonstrates that spectral imaging has a high potential to identify wheat classes non-destructively.

Reflectance spectrometry and multispectral data for mapping fractures and hydrothermal alterations in the northern edge of the eastern High Atlas, Morocco

The Marginal Folds, in the north-western axis of Moroccan eastern High Atlas, are mainly composed of the Infra-Cenomanian terrains. These latter contain continental detrital deposits (red beds) and are characterized by a network of fractures. These marginal folds host significant concentrations of metallic minerals, such as Cu, Pb, Ag, and Zn, particularly at the Merija (Cu) and Bou Sellam (Pb-Zn) sites.Several techniques were applied to the Landsat-8 Oli images to improve the clarity and visibility of the linear features. Radiometric and atmospheric corrections, color compositions, directional filters, and principal component analysis were adopted. The lineaments resulting from automatic extraction, as well as their statistical analysis were validated by visual interpretation and field investigations. Statistical analysis revealed three dominants fracture systems: N-S, NE-SW, and E-W; the first two systems show a high density in the study area.The mineral alteration of the outcropping rocks is well described by ASTER multispectral imaging techniques. In this work, the effectiveness of this techniques for mineral alteration mapping was demonstrated on the Merija area. Several occurrences of mineral alteration were highlighted, using a combination of false color and band ratios, the principal component analysis (PCA), and the band ratios. This research was completed by reflectance spectrometry, covering the visible, near-infrared (VNIR), and short-wave infrared (SWIR) domains, to identify mineral alteration.Hydrothermal alteration in the Merija region includes clay, phyllite, carbonate, and phyllosilicate alteration, such as kaolinite, illite, montmorillonite, palygorskite, and vermiculite. Regarding to oxidation, as evidenced by the presence of goethite, jarosite, and hematite, it is widespread in most of the analyzed samples, resulting from the alteration of pyrite, followed by jarosite and goethite.

Leveraging visible-near-infrared spectroscopy and machine learning to detect nickel contamination in soil: Addressing class imbalances for environmental management

Excessive concentrations of Ni in soil have many severe effects, negatively affecting human health and leading to disease, while also posing a threat to animals and plants. Although the dangers of high Ni concentrations have been widely recognized, rapid and large-scale tools for the identification of Ni contamination are still lacking. Visible-near-infrared (Vis-NIR) spectroscopy has been employed to rapidly identify Ni contamination; however, previous studies suffer from issues inherent to small datasets and the tendency to negate data imbalances. To address these issues, a large dataset comprising 18,675 soil samples was used to predict soil Ni contamination by combining Vis-NIR data with machine learning (ML). The data imbalance inherent to previous studies was addressed using two data sampling methods. To build a robust classification model for Ni contamination, four spectral preprocessing methods and four ML algorithms were compared. The optimal extreme gradient boosting model achieved recall, accuracy, area under the curve, and geometric mean scores of 0.8203, 0.8806, 0.9268, and 0.8508, respectively. Model predictions across the United States identified specific regions with high possibility of Ni contamination. Overall, the model developed in this study offers an improved accuracy in predicting soil Ni contamination at the continental scale, and can be used to prioritize further testing and guide policymaking.

Advanced Dual‐Band Smart Windows: Inorganic All‐Solid‐State Electrochromic Devices for Selective Visible and Near‐Infrared Modulation

AbstractDual‐band electrochromic smart windows (DESWs), capable of actively and selectively modulate visible (VIS) light and near‐infrared (NIR) heat, have emerged as a practical technology for enhancing user comfort and reducing building energy consumption. However, the design and development of DESWs remain a significant challenge due to the difficulty in obtaining suitable materials and high‐durability electrolytes. Here, the first all‐solid‐state DESW based on an orthorhombic oxygen‐deficient tungsten oxide (o‐WO3‐x) film is presented. Benefiting from the synergistic effects of the efficient oxygen‐vacancy‐enhanced charge transfer process and the secure transfer pathway enabled by the orthorhombic crystal structure, the o‐WO3‐x film showcases remarkable dual‐band electrochromic properties, including selective modulation of VIS light and NIR heat, large optical modulation (89.1%), rapid response time (tb/tc = 6.8/17.9 s), high coloration efficiency (155.92 cm2 C−1), and ultrastable cyclic performance (8000 cycles) even in acidic aqueous electrolyte. Furthermore, the all‐solid‐state DESWs incorporating o‐WO3‐x deliver a significant and stable dual‐band electrochromic response with excellent thermal regulation and energy‐saving capabilities. These findings underscore the considerable potential of o‐WO3‐x films and their all‐solid‐state smart windows in decreasing building energy consumption.

Precision variety identification of shelled and in-shell pecans using hyperspectral imaging with machine learning

Pecans are essential nuts containing polyunsaturated fatty acids and dietary fiber which offer health benefits to humans. They are exported and sold in both in-shell and shelled forms. However, varietal identification poses a challenge to both producers and processors, which results in variety substitution for economic advantages. The aim of this study was to investigate the efficacy of visible near-infrared (VNIR) and near-infrared (NIR) hyperspectral imaging systems (HSI) to accurately identify pecan cultivars (Cape fear, Desirable, Stuart blend, and Sumner). The in-shell and shelled spectra were acquired using VNIR and NIR-HSI at wavelengths 400–1000 nm and 900–1700 nm, respectively. The spectra dimensionality was reduced using principal component analysis (PCA). Thereafter, the selected principal components (PCs) were used to build six machine learning classifiers (Decision Tree, Random Forest, Gradient Boosting, Partial Least Square Discriminant Analysis, Support Vector Machine, and Linear Discriminant Analysis (LDA)) for four-class classification. LDA with and without PCA achieved the highest accuracy for both pecan forms. For shelled pecans, the LDA without PCA achieved 90.59 % and increased to 91.67 % accuracy with PCA on the VNIR spectra, while the LDA without PCA achieved 93.36 % and increased to 93.52 % accuracy with PCA on the NIR spectra. For the in-shell pecans, LDA without PCA achieved 98.59 % and increased to 99.12 % accuracy with PCA on the VNIR spectra, while LDA with and without PCA achieved 98.26 % accuracy for the NIR spectra. Moreover, Successive Projection Algorithm was also implemented for wavelength selection and modeling with satisfactory results. Overall, higher accuracy was achieved in the in-shell pecan. This study revealed the usefulness of HSI systems in identifying pecan varieties.

In-situ prediction of soil organic carbon contents in wheat-rice rotation fields via visible near-infrared spectroscopy

Visible near-infrared (VNIR) spectroscopy is a reliable method for estimating soil properties. However, its effectiveness in accurately predicting soil organic carbon (SOC) contents, particularly in wheat-rice rotation fields, remains uncertain. In this study, we collected 202 samples from wheat-rice fields (0–20 ​cm) in southeastern China and measured in-situ spectra of the vertical surface of the soil cores and the laboratory spectra of the dried and sieved soil samples. Our study focused on evaluating three algorithms - external parameter orthogonalization (EPO), direct standardization (DS), and piecewise direct standardization (PDS) - to address the influence of external factors, particularly soil moisture. To carry out our analysis, the dataset was divided into calibration (141 samples) and validation (61 samples) sets via the Kennard-Stone algorithm. A subset of the corresponding in-situ and laboratory spectra in the calibration set (transfer set) was used to derive the transfer matrix for EPO, DS, and PDS, enabling the conversion of in-situ spectra to laboratory spectra by characterizing their differences. Four machine learning models, including cubist, partial least squares regression (PLSR), random forest (RF), and memory-based learning (MBL), were used to predict the SOC, particulate organic carbon (POC), and mineral-associated organic carbon (MAOC) contents based on the laboratory, in-situ, and corrected in-situ spectra. The results revealed that the laboratory spectra outperformed the non-corrected in-situ spectra, with coefficients of determination (R2) of 0.91, 0.75, and 0.80 for SOC, POC, and MAOC, respectively. Among the models, MBL and PLSR exhibited the highest average R2 at 0.85–0.86. EPO marginally improved the prediction accuracy (R2 increased from 0.85 to 0.87 for SOC, 0.64 to 0.69 for POC, and 0.75 to 0.82 for MAOC). These promising prediction accuracies underscore the potential of VNIR spectra for in-situ predictions in wheat-rice fields in Southeast China, offering insights for predicting SOC contents via in-situ spectroscopy.

Broadening the scope of food quality control: A calibration transfer strategy for minimizing external influences on spectral analysis

Calibration transfer (CT) is an effective tool to address spectral inconsistencies and the decline in model prediction capabilities caused by various influences. This study introduced a modified semi-supervised parameter-free calibration enhancement (MSS-PFCE) strategy to enable calibration models extensively applicable across varying temperatures, instruments and orientations. Investigations conducted on three datasets of watermelon juice, corn, and apples were utilized to validate the efficacy of MSS-PFCE. The results showed that MSS-PFCE decreased the average RMSEP for slave spectrum predictions by 96.51%, 75.96%, and 23.04%, respectively. MSS-PFCE significantly outperformed the original SS-PFCE approach and three classical standardization methods (direct standardization (DS), piecewise direct standardization (PDS) and slope and bias correction (SBC)). Furthermore, MSS-PFCE required no standards and needed only 5%–10% of slave samples for CT, maintaining robust reliability across varying sample sizes and cost thresholds (cth). The conclusions were further examined in three additional datasets with these variations. Notably, MSS-PFCE exhibited high accuracy, fitting stability, low sample dependency, and versatile transferability, positioning it as a promising candidate for widespread implementation in visible/near-infrared (Vis/NIR) spectroscopy. This study has significant potential to enhance the applicability of quality analysis, broaden the scope of food quality control, and promote the sound development of the food industry.

Evaluation of a hyperspectral image pipeline toward building a generalisation capable crop dry matter content prediction model

Hyperspectral imaging has proven to be a reliable technique for estimating dry matter, a common variable when considering the quality of the fresh produce. However, developing models capable of generalising across different crops is challenging. In this study, several pipelines were explored towards achieving a robust and accurate generic regression model were evaluated and the development of Automatic Relevance Determination (ARD) and Partial Least Squares (PLS) algorithms for fruit and vegetable dry matter estimation. The models were built using a VIS-NIR dataset that includes both fruit and vegetables, namely, apples, broccoli and leek (n = 779). The PLS regression model obtained Root Mean Square on Prediction (RMSEP) = 0.0137, outperforming ARD regression (RMSEP = 0.0140) on a 10x5-fold cross-validation protocol. The evaluated preprocessing techniques affect the two regression algorithms differently, with the best results achieved when the pipeline was used without feature extraction. Overall, the pipeline using either ARD or PLS regression shows strong performance and generalisation for Visible-Near Infrared (VIS-NIR)-based dry matter estimation across diverse fruits and vegetables.

Reusing the Wasted Energy of Electrochromic Smart Window for Near-Zero Energy Building.

Electrochromic smart windows (ESWs) are an effective energy-saving technology for near-zero energy buildings. They consume electric energy unidirectionally during a round-trip coloring-bleaching process, with the energy involved in the bleaching process being wasted. It is highly desirable to reuse this wasted electric energy directly and/or transfer it into other energy storage equipment, further enhancing the overall efficiency of electric energy usage. Herein, a zinc anode-based ESW (ESW-PZ) is reported that not only has fascinating visible-near-infrared (VIS-NIR) dual-band electrochromic performance (a high optical contrast of 63%) but also showcases good energy storage characteristics (a wide voltage window of 2.6V and a high energy density of 127.5 µWh cm-2). The buildings utilizing ESW-PZ to modulate indoor environments demonstrated an average annual energy saving of 366MJ m-2 based on energy simulations, which is about 16% of the total energy consumption. Impressively, a high utilization efficiency of 90% (855 mWh m-2) of the wasted electric energy is realized through an ingenious circuit-switching strategy, which can be reused to power small household appliances.

Online detection of moldy apple core based on diameter and SSC features

Moldy core is a common internal disease in apples, with current visible/near-infrared (Vis/NIR) spectroscopy-based detection methods often suffering from inaccuracies due to variations in fruit diameter and soluble solids content (SSC). To address this issue, an online detection system was developed that integrates spectral corrections for both diameter and SSC information. The spectra were first corrected using a hyperbolic sine function based on fruit diameter, followed by a second correction utilizing the spectral features of SSC through the Variational Mode Decomposition (VMD) algorithm. This two-step correction process effectively mitigated the influence of diameter and SSC variations on the spectral data. A Partial Least Squares Discriminant Analysis (PLS-DA) model was subsequently constructed using the corrected spectra and moldy core labels. Compared to the uncorrected model, the improved model exhibited substantial enhancements in accuracy, recall, and precision, achieving 94.44%, 92.59%, and 96.15%, respectively. Additionally, online validation with independent samples demonstrated an accuracy of 88.33%, highlighting the system's stability and robustness for efficient online detection of apple moldy core.

Research on prediction of yellow flesh peach firmness using a novel acoustic real-time detection device and Vis/NIR technology

Firmness is a critical indicator for predicting fruit ripeness, optimal harvest date, and shelf life. In this study, a novel fruit acoustic real-time detection prototype device and a conventional visible near-infrared (Vis/NIR) spectroscopy real-time detection device were used to collect acoustic and spectral signals from yellow flesh peaches to jointly predict their firmness. The acoustic and optical signals were generated into one- and two-dimensional feature data by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), continuous wavelet transform (CWT) and Gramian angular field (GAF) data processing methods. Based on these data, a variety of yellow flesh peach firmness prediction models were constructed in this study, including partial least square (PLS), support vector regression (SVR), Swin Transformer (SwinT), and SwinT-PLS/SVR. The experimental results showed that the SwinT-PLS model based on the fusion of competitive adaptive re-weighted sampling (CARS)-acoustic image features and CARS-Vis/NIR spectral features showed the best prediction performance (R2P = 0.951, the RMSEP = 0.443 N/mm, RPDP = 4.339), and the prediction performance is significantly higher than that of the prediction model based on single acoustic and Vis/NIR spectral data. The method proposed can fast, non-destructively, accurately predict fruit firmness and has excellent prospects for commercial real-time fruit sorting applications.