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Nondestructive egg freshness assessment from the equatorial and blunt region based on visible near infrared spectroscopy
摘要： This research was to study which orientation was better for freshness prediction of the white-shelled eggs using visible near infrared spectroscopy. The transmission spectra were acquired in the equatorial region and at the blunt end of the eggs. After each spectral measurement, the Haugh unit, yolk index, and albumen pH as the freshness parameters were simultaneously measured using traditional destructive methods. Pretreatment methods containing Savitzky-Golay smoothing, multiplicative scatter correction, the standard normal variate, the first derivative and the second derivative were used. A partial least squares regression was developed to predict the Haugh unit, yolk index, and albumen pH. The best correlation coefficients of prediction were obtained from the equatorial region, and were 0.881, 0.855, and 0.888 for the Haugh unit, yolk index and albumen pH, respectively. And root mean square errors in the prediction set were 7.720, 0.034, and 0.147 for the Haugh unit, yolk index and albumen pH, respectively. The results illustrated that the equatorial region showed better ability than the blunt end to predict freshness of the white-shelled eggs.
关键词： nondestructive,visible near infrared spectroscopy,orientation,freshness,Egg
更新于2025-09-23 15:22:29
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A Nondestructive Real-Time Detection Method of Total Viable Count in Pork by Hyperspectral Imaging Technique
摘要： A nondestructive method was developed for assessing total viable count (TVC) in pork during refrigerated storage by using hyperspectral imaging technique in this study. The hyperspectral images in the visible/near-infrared (VIS/NIR) region of 400–1100 nm were acquired for fifty pork samples, and their VIS/NIR diffuse reflectance spectra were extracted from the images. The reference values of TVC in pork samples were determined by classical microbiological plating method. Both partial least square regression (PLSR) model and support vector machine regression model (SVR) of TVC were built for comparative analysis to achieve better results. Different transformation methods and filtering methods were applied to improve the models. The results show that both the optimized PLSR model and SVR model can predict the TVC very well, while the SVR model based on second derivation was better, which achieved with RP (correlation coefficient of prediction set) = 0.94 and SEP (standard error of prediction set) = 0.4570 log CFU/g in the prediction set. An image processing algorithm was then developed to transfer the prediction model to every pixel of the image of the entire sample; the visualizing map of TVC would be displayed in real-time during the detection process due to the simplicity of the model. The results demonstrated that hyperspectral imaging is a potential reliable approach for non-destructive and real-time prediction of TVC in pork.
关键词： visible/near-infrared,total viable count,pork,hyperspectral imaging
更新于2025-09-23 15:22:29
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Identification and diagnosis of whole body and fragments of Trogoderma granarium and Trogoderma variabile using visible near infrared hyperspectral imaging technique coupled with deep learning
摘要： The khapra beetle, Trogoderma granarium Everts, is the most critical biosecurity pest threat which threatens the grains industry worldwide. To prevent incursion of the khapra beetle, very accurate and reliable diagnostic tools are required to differentiate the khapra beetle from other morphologically, closely related Trogoderma sp., in particular the larva stage. However, at present, it can only be identified by highly skilled taxonomists. Furthermore, often suspected Trogoderma sp. found in grain products are the body fractions such as larval skins or fragmented adult, which are impossible to diagnose morphologically. This work explored the combination of visible near infrared hyperspectroscopy (VNIH) and deep learning tools to identify the khapra beetle. About 2000 hyperspectral images were acquired under this study. Images of T. granarium and Trogoderma variabile, adult, larvae, larvae skin, fragments of adult and larvae images, were subjected to two deep learning models; Convolutional Neural Networks (CNN) and Capsule Network for analysis. Overall, above 90% accuracy was obtained with both models, whereas Capsule Network achieved a higher accuracy of 96%. For whole adult body and adult fragments, the accuracy achieved was 96.2% and 91.7%, respectively. For whole larvae, larvae skin and larvae fragment, accuracies of 93.4%, 91.6%, and 90.3% were achieved. Ventral orientation gave better accuracy over dorsal orientation of the insects for both larvae and adult stages. Based on the above results, VNIH imaging technology coupled with appropriate machine learning tools can be used to identify one of the most notorious stored grain pests, the khapra beetle, from other morphologically similar Trogoderma sp like T. variabile. Particularly, the technology offers a new approach and possibility of an effective identification of Trogoderma sp. from its body fragments and larvae skins, which are otherwise impossible to diagnose taxonomically.
关键词： Deep learning,Visible near infrared hyperspectroscopy (VNIH),Trogoderma diagnostic,Capsule network,Convolutional neural networks
更新于2025-09-23 15:19:57
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Maintaining the predictive abilities of egg freshness models on new variety based on VIS-NIR spectroscopy technique
摘要： This research was performed to study calibration model transfer between White Leghorns eggs and Bantam eggs for prediction of egg freshness by visible near infrared (VIS-NIR) spectroscopy. Transmission spectra of the two varieties were acquired in the equatorial region of the eggs. And albumen pH as the freshness evaluating parameter was measured using traditional destructive methods. After outliers were eliminated by Mahalanobis distance combined with principal component analysis (PCA), partial least squares regression (PLSR) with different preprocessing methods was used to develop prediction models. Global updating, direct standardization (DS) and slope/bias correction (SBC) were evaluated to transfer calibration models from one variety to another. The Kennard-Stone (KS) algorithm was used to select standardization samples. White Leghorns eggs and Bantam eggs as the master variety in turn were compared to find superior master variety. Application of the slope/bias correction (SBC) algorithm obtained the best prediction results of albumen pH. And the better slope/bias correction (SBC) transfer performance with a rp of 0.908 and a RMSEP of 0.133 was found when Bantam eggs were as the superior master variety.
关键词： Slope/bias correction,Visible near infrared spectroscopy,Direct standardization,Global updating,Egg
更新于2025-09-19 17:15:36
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EXPRESS: Use of Visible–Near-Infrared (Vis–NIR) Spectroscopy to Detect Aflatoxin B <sub/>1</sub> on Peanut Kernels
摘要： Current methods for detecting aflatoxin contamination of agricultural and food commodities are generally based on wet chemical analyses, which are time-consuming, destructive to test samples and require skilled personnel to perform, making them impossible for large-scale nondestructive screening and on-site detection. In this study, we utilized visible–near-infrared (Vis–NIR) spectroscopy over the spectral range of 400–2500 nm to detect contamination of commercial, shelled peanut kernels (runner type) with the predominant aflatoxin B1 (AFB1). The artificially contaminated samples were prepared by dropping known amounts of aflatoxin standard dissolved in methanol, onto peanut kernel surface to achieve different contamination levels. The partial least squares discriminant analysis (PLS-DA) models established using the full spectra over different ranges achieved good prediction results. The best overall accuracy of 88.57% and 92.86% were obtained using the full spectra when taking 20 and 100 parts per billion (ppb), respectively, as the classification threshold. The random frog (RF) algorithm was used to find the optimal characteristic wavelengths for identifying the surface AFB1-contamination of peanut kernels. Using the optimal spectral variables determined by the RF algorithm, the simplified RF-PLS-DA classification models were established. The better RF-PLS-DA models attained the overall accuracies of 90.00% and 94.29% with the 20 ppb and 100 ppb thresholds, respectively, which were improved compared to using the full spectral variables. Compared to using the full spectral variables, the employed spectral variables of the simplified RF-PLS-DA models were decreased by at least 94.82%. The present study demonstrated that the Vis–NIR spectroscopic technique combined with appropriate chemometric methods could be useful in identifying AFB1 contamination of peanut kernels.
关键词： Vis–NIR,PLS-DA,peanut kernel,visible–near-infrared spectroscopy,random frog,Aflatoxin,partial least squares discriminant analysis
更新于2025-09-19 17:15:36
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Wearable-band Type Visible-Near Infrared Optical Biosensor for Non-invasive Blood Glucose Monitoring
摘要： Diabetes is a worldwide-serious problem that can only be delayed or prevented by a regular monitoring of blood glucose (BG) concentration level. Continuous monitoring systems allow subjects to prepare the diabetes management strategy and prevent the long-term complications diseases. Until now, most studies utilize various biofluids such as sweat, tears and saliva that have serious unresolved setback such as expensive material, sensor stability, sensor calibration and long-settling time. Therefore, we developed a novel BG sensor which is cost efficient and highly wearable with a small data acquisition time window that allow a non-invasive, long-term continuous blood glucose monitoring (CGM) system. The novel biosensor exploits a unique information of the pulsatile to continuous components of the arterial blood volume pulsation during the change of blood glucose (BG) concentration at the wrist tissue. The reflected optical signal was measured in the combine visible-near infrared (Vis-NIR) spectroscopy. An in-vivo experiment which enclosed 12 volunteers in a two-hour modified carbohydrate-rich meals reached the average correlation coefficient (????) between the estimated and reference BG concentration of 0.86, with the standard prediction error (SPE) of 6.16 mg/dl. Moreover, the full-day experiment was also conducted to test the reliability of the proposed sensor. Results showed that the created model in the previous day, may estimate a full-day BG concentration which was done in next day with an adequate performance.
关键词： Wearable Sensor,Optical Biosensor,Noninvasive Measurement,Visible-Near Infrared Spectroscopy,Diabetes,Continuous Blood Glucose Monitoring
更新于2025-09-19 17:15:36
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Broadband visible-near infrared and deep ultraviolet generation by four-wave mixing and high-order stimulated Raman scattering from the hybrid metasurfaces of plasmonic nanoantennae and Raman-active nanoparticles
摘要： The efficient generation of a broadband frequency comb from the visible to ultraviolet region is a challenging task despite its importance for nanoscale spectroscopy and sensing applications. In this paper, we reported broadband visible-near infrared and deep ultraviolet generation by four-wave mixing and high-order stimulated Raman scattering from hybrid metasurfaces made of plasmonic nanoantennae embedded with Raman-active diamond nanoparticles as examples. Upon two-color near-infrared pumping tuned to a Raman resonance, one can generate a visible-near infrared frequency comb with a major contribution of high-order stimulated Raman scattering by the coherent modulation of the Raman medium and simultaneously, a broad deep ultraviolet frequency comb is radiated by four-wave mixing and third-harmonic generations. The efficiencies of the individual spectral peaks reached values in the order of 10?8–10?2% under pumping with pulses with a peak intensity of about 33 GW cm?2 and a duration of 100 fs in the near infrared region.
关键词： high-order stimulated Raman scattering,visible-near infrared,hybrid metasurfaces,plasmonic nanoantennae,Raman-active diamond nanoparticles,deep ultraviolet,broadband frequency comb,four-wave mixing
更新于2025-09-12 10:27:22
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Soil organic carbon predictions in Subarctic Greenland by visible–near infrared spectroscopy
摘要： Release of carbon from high-latitude soils to the atmosphere may have significant effects on Earth’s climate. In this contribution, we evaluate visible–near-infrared spectroscopy (vis-NIRS) as a time- and cost-efficient tool for assessing soil organic carbon (SOC) concentrations in South Greenland. Soil samples were collected at two sites and analyzed with vis-NIRS. We used partial least square regression (PLS-R) modeling to predict SOC from vis-NIRS spectra referenced against in situ dry combustion measurements. The ability of our approach was validated in three setups: (1) calibration and validation data sets from the same location, (2) calibration and validation data sets from different locations, and (3) the same setup as in (2) with the calibration model enlarged with few samples from the opposite target area. Vis-NIRS predictions were successful in setup 1 (R2 = 0.95, root mean square error of prediction [RMSEP] = 1.80 percent and R2 = 0.82, RMSEP = 0.64 percent). Predictions in setup 2 had higher errors (R2 = 0.90, RMSEP = 7.13 percent and R2 = 0.78, RMSEP = 2.82 percent). In setup 3, the results were again improved (R2 = 0.95, RMSEP = 2.03 percent and R2 = 0.77, RMSEP = 2.14 percent). We conclude that vis-NIRS can obtain good results predicting SOC concentrations across two subarctic ecosystems, when the calibration models are augmented with few samples from the target site. Future efforts should be made toward determination of SOC stocks to constrain soil–atmosphere carbon exchange.
关键词： visible–near-infrared spectroscopy,subarctic,Soil organic carbon,Greenland
更新于2025-09-12 10:27:22
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Subwavelength polarization optics via individual and coupled helical traveling-wave nanoantennas
摘要： Soil spectral allocation or classification is usually conducted on air-dried soils. However, the field soils are not all air-dried, and the change of soil moisture will affect soil reflectance. We introduce a soil allocation model that considers the effect of soil moisture for the purpose of eliminating the effect of soil moisture. The topsoil spectral curves of four typical soils from the Songnen Plain in Northeast China were re-sampled to 10-nm intervals and converted to first-derivative spectral curves and continuum removal curves. The spectral feature parameters were extracted from continuum removal curves in the visible-near infrared (VNIR) range (350–2500 nm), and the range of 430–2400 nm was used to build soil allocation models for reducing the effect of noise. Samples with different soil moisture were mixed into air-dried soils and we calculated the coefficient of variation (CV) of different inputs to assess the effect of soil moisture and to find allocation indices that were not affected by soil moisture. We used allocation indices of Zhang et al. (2018) because of the high accuracy of their DT (Decision Tree) model to allocate mixed-soil samples. We also used allocation indices that were not affected by soil moisture to allocate mixed-soil samples with decision tree (DT), multinomial logistic regression (MLR) and multi-layer perception neural network (MLPNN), and compared the results of the two methods. The results show the following: 1) As SFPs were built with shorter bands, SFP was less sensitive to soil moisture than PCR and PCFD and thus SFP is more suitable to build soil allocation models that consider the effect of soil moisture as input than PCR and PCFD. 2) Differences in soil moisture had little effect on absorption valley shoulders, symmetry and absorption positions, moderate effect on absorption area and depth, and a major effect on the slope of different bands. 3) The effect of soil moisture on continuum removal curves of different soil classes was variable. There was little effect on Arenosols, a moderate effect on Chernozems and Cambisols, and a large effect on Phaeozems. 4) The accuracy of the DT model using allocation indices that were not affected by soil moisture was 91.892% with a Kappa coefficient of 0.888. Our results suggest that it is feasible to build soil spectral allocation models that are not affected by soil moisture, and this improves the universality of soil spectral allocation, especially to field soils, which can be of considerable help in soil classification.
关键词： Decision tree,Visible-near infrared,Soil moisture,Spectral feature parameter,Soil spectral allocation
更新于2025-09-11 14:15:04
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Visible-Near-Infrared Spectroscopy Prediction of Soil Characteristics as Affected by Soil-Water Content
摘要： Soil physical characteristics are important drivers for soil functions and productivity. Field applications of near-infrared spectroscopy (NIRS) are already deployed for in situ mapping of soil characteristics and therefore, fast and precise in situ measurements of the basic soil physical characteristics are needed at any given water content. Visible-near-infrared spectroscopy (vis–NIRS) is a fast, low-cost technology for determination of basic soil properties. However, the predictive ability of vis–NIRS may be affected by soil-water content. This study was conducted to quantify the effects of six different soil-water contents (full saturation, pF 1, pF 1.5, pF 2.5, pF 3, and air-dry) on the vis–NIRS predictions of six soil physical properties: clay, silt, sand, water content at pF 3, organic carbon (OC), and the clay/OC ratio. The effect of soil-water content on the vis–NIR spectra was also assessed. Seventy soil samples were collected from five sites in Denmark and Germany with clay and OC contents ranging from 0.116 to 0.459 and 0.009 to 0.024 kg kg-1, respectively. The soil rings were saturated and successively drained/dried to obtain different soil–water potentials at which they were measured with vis–NIRS. Partial least squares regression (PLSR) with leave-one-out cross-validation was used for estimating the soil properties using vis–NIR spectra. Results showed that the effects of water on vis–NIR spectra were dependent on the soil–water retention characteristics. Contents of clay, silt, and sand, and the water content at pF 3 were well predicted at the different soil moisture levels. Predictions of OC and the clay/OC ratio were good at air-dry soil condition, but markedly weaker in wet soils, especially at saturation, at pF 1 and pF 1.5. The results suggest that in situ measurements of spectroscopy are precise when soil-water content is below field capacity.
关键词： Visible-Near-Infrared Spectroscopy,Soil Physical Properties,Soil Characteristics,Soil-Water Content,Partial Least Squares Regression
更新于2025-09-09 09:28:46