Short-wave NIR Research Articles

Short wave-near infrared spectroscopy for predicting soluble solid content in intact mango with variable selection algorithms and chemometric model

Mango sweetness is one of the most prominent internal quality constituents that attract consumer's attention. The current common methods used to determine the sweetness of mango have significant disadvantages in that labor-intensive, time-consuming, and damaging techniques. This research aims to predict sweetness in mangoes using short wave-near infrared (SW-NIR) spectroscopy in the ranges 900–1650 nm, along with variable selection algorithms and chemometric model. A total of 120 mango samples were used to collect the spectra using a fibre module SW-NIR spectrometer. The partial least squares (PLS) regression with several spectral preprocessing methods was employed to develop the calibration model, and the best preprocessing technique was selected. The Savitzky–Golay second-derivative preprocessing technique performed better among the other preprocessing techniques with a correlation coefficient of prediction (rpred) of 0.74 and standard error of prediction (SEP) is 0.78 %Brix. After that, two variable selection techniques were used to select effective wavelength variables, including regression coefficient and successive projections algorithm (SPA). For SSC prediction in the range 900–1650 nm, the SPA-PLS model obtained a rpred of 0.78 and SEP of 0.67 %Brix. The current study unequivocally shows that the proposed SW-NIR spectroscopy coupled with a suitable chemometrics method can evaluate mango sweetness nondestructively.

External electric field induced emission behavior for ESIPT-based 2-(benzo[d]thiazol-2-yl)-4-(pyren-1-yl)phenol towards near-infrared region

Organic light-emitting diodes (OLEDs) for low energy transfer and double emission, but the current methods for regulating ESIPT processes are mostly solvent and substituent effects. Here, utilizing the density theory functional (DFT) and time-dependent density functional theory (TD-DFT) methods, the ESIPT process controlled by an external electric field (EEF) is proposed, and the changes in photophysical properties of 2-(benzo[d]thiazol-2-yl)-4-(pyren-1-yl)phenol (PyHBT) are investigated. Structural parameter variations and IR vibrational spectra measure the prerequisite for the ESIPT process, namely, intramolecular hydrogen bond (IHB) strength, and the scanned potential energy curves (PECs) demonstrate that the ESIPT process of PyHBT is harder to execute as the positive EEF increases, and the opposite is true for the negative EEF. The absorption and fluorescence spectra show shifts under the distinct EEFs, and even the emission wavelength reaches the short-wave near-infrared (SW-NIR) region (780–1100 nm), such as 815.2 nm for a positive EEF of + 30 × 10-4 a.u. in the keto form. Additionally, the fluorescence intensity of PyHBT is strongly influenced by the positive EEF, especially in the enol form, and the investigation of the mechanism by hole-electron analysis demonstrates that under the positive EEF, the twisted intramolecular charge transfer (TICT) process is induced, which triggers the weakening of the fluorescence intensity. In summary, our work not only complements the theoretical approach to modulate the ESIPT process, but also reveals that the photophysical properties of materials affected by the external electric field are even expected to reach the NIR region.

In Situ Investigation of Defect Site Formation in Carbon Nanotubes

Semiconducting single-walled carbon nanotubes (SWCNTs) exhibit a photostable excitonic fluorescence in the short wave near-infrared (SWIR). Recently, the covalent functionalization of nanotubes opened new avenues to manipulate their excitonic fluorescence [1]. These “organic color centers (OCCs)” act as localized potential wells on the nanotube surface, trapping locally otherwise diffusing excitons. This effectively results in an increase in the SWCNT emission, the trapped excitons avoiding possible quenching sites along the nanotube surface (structural defects, nanotube ends) [2]. This new radiative pathway results in a shifting of the SWCNT emission towards longer wavelengths and allows SWCNT excitation on their first-order excitonic transition (ca. SWIR) [3]. Altogether, these properties make OCC-functionalized SWCNTs particularly attractive for various applications ranging from biological imaging to quantum information. Yet, the functionalization reaction of SWCNTs with OCC is still poorly understood, stochastic and, as a result, poorly controlled. To gain knowledge and control over these reactions, we developed a two-color imaging platform to image simultaneously both the SWCNT and OCC emissions in situ during the functionalization reaction [4]. We will present how this platform allows to monitor the kinetics of the functionalization reaction and further discuss our findings which provide insights on the interactions existing between OCCs and the SWCNT surface.[1] A. H. Brozena et al., Nat. Rev. Chem., vol. 3, no. 6, pp. 375–392, 2019[2] N. Danné et al., ACS Nano, vol. 12, no. 6, pp. 6059–6065, 2018[3] A. K. Mandal et al., Sci. Rep., vol. 10, no. 1, pp. 1–9, 2020[4] B.P. Lambert et al., in preparation

Detecting bioactive compound contents in Dancong tea using VNIR-SWIR hyperspectral imaging and KRR model with a refined feature wavelength method

Hyperspectral imaging (HSI) provides opportunity for non-destructively detecting bioactive compounds contents of tea leaves and high detection accuracy require extracting effective features from the complex hyperspectral data. In this paper, we proposed a feature wavelength refinement method called interval band selecting-competitive adaptive reweighted sampling-fusing (IBS-CARS-Fusing) to extract feature wavelengths from visible-near-infrared (VNIR) and short-wave-near-infrared (SWIR) hyperspectral images. Combined with the proposed IBS-CARS-Fusing method, a kernel ridge regression (KRR) model was established to predict the contents of bioactive compounds including chlorophyll a, chlorophyll b, carotenoids, tea polyphenols, and amino acids in Dancong tea. It was revealed that the IBS-CARS-Fusing method can improve Rp2 of KRR model for these bioactive compounds by 4.77%, 4.60%, 6.74%, 15.52%, and 13.10%, respectively, and Rp2 of the model reached high values of 0.9500, 0.9481, 0.8946, 0.8882, and 0.8622. Additionally, a leaf compound mass per area thermal map was used to visualize the spatial distribution of the compounds.

Applications and Future Prospects of Near-Infrared Light Technology in the Beauty and Skincare Field: A Comprehensive Review

This review aims to explore the effects and applications of near-infrared (NIR) light in the beauty and skincare field, with a focus on the advantages of combining long-wave and short-wave NIR technologies. It also presents the development and application of an innovative home beauty device. Firstly, the paper introduces the definition and classification of NIR light, explaining its biological effects and broad applications in medical, industrial, scientific research, and beauty fields. Next, it provides a detailed analysis of the current use of NIR light in skincare, including its physiological effects on the skin and the benefits and performance of existing technologies and devices. Despite its significant advantages in beauty and skincare, NIR light has limitations such as limited penetration depth, single-functionality, and safety concerns. To overcome these limitations, the paper highlights the benefits of combining long-wave and short-wave NIR technologies, such as enhancing skin absorption, improving treatment efficacy, and reducing side effects. The review includes a case study of a home beauty device to illustrate these benefits. Finally, the paper summarizes the key findings of combined NIR technologies and explores the market potential of home beauty devices. It also discusses future research directions and prospects in the skincare field, providing valuable insights for the further development of efficient and safe skincare technologies. The review shows that NIR light has a promising future in the beauty and skincare industry.

Application of a short-wave pocket-sized near-infrared spectrophotometer to predict milk quality traits

Portable-hand-held devices based on near-infrared (NIR) technology have improved and are gaining popularity, even if their implementation in milk has been barely evaluated. Thus, the aim of the present study was to assess short-wave pocket-sized NIR devices' feasibility to predict milk quality. A total of 331 individual milk samples from different cow breeds and herds were collected in 2 consecutive days for chemical determination and spectral collection by using 2 pocket-sized NIR spectrophotometers working in the range of 740-1070 nm. The reference data was matched with the corresponding spectrum and modified-partial least-squares regression models were developed. A 5-fold cross-validation was applied to evaluate individual devices' performance and an external-validation with 25% of the data set as the validation set was applied for the final models. Results revealed that both devices' absorbance was highly correlated but greater for instrument A than B. Thus, the final models were built by averaging the spectra from both devices for each sample. The fat content prediction model was adequate for quality control with a coefficient of determination (R2ExV) and a residual predictive deviation (RPDExV) in external validation of 0.93 and 3.73, respectively. Protein and casein content as well as fat-to-protein ratio prediction models might be used for a rough screening (R2ExV > 0.70; RPDExV > 1.73). However, poor prediction models were obtained for all the other traits with an R2ExV between 0.43 (urea) and 0.03 (somatic cell count), and a RPDExV between 1.18 (urea) and 0.22 (somatic cell count). In conclusion, short-wave portable-hand-held NIR devices accurately predicted milk fat content, and protein, casein, and fat-to-protein ratio might be applied for rough screening. It seems that there is not enough information in this NIR region to develop adequate prediction models for lactose, somatic cell count, urea, and freezing point.

Nondestructive prediction of lime acidity with a single scan using two types of near infrared spectrometers and ensemble learning strategy

An accurate and nondestructive prediction method of lime acidity was established based on near infrared (NIR) spectroscopy and ensemble learning strategy. Dual-band spectra were obtained nondestructively with a single scan using a desk-top Fourier transform spectrometer and a grating portable spectrometer. Spectral preprocessing methods were used to eliminate the interferences in the spectra. The quantification models of available acidity (pH and 10−pH) and total acidity (TA) were developed with the ensemble learning strategy compared with partial least squares (PLS) and variable selection methods. The results indicated that due to the high-energy light source, the models of the grating portable spectrometer were much better than those of the Fourier transform spectrometer. Short-wave NIR (SWNIR) was more suitable for quantitative analysis of available acidity, while long-wave NIR (LWNIR) was more effective for quantitative analysis of TA. Besides, the models of available acidity were ahead of those of TA. Compared with PLS and variables selection methods, the ensemble learning strategy can produce models with higher prediction accuracy and better robustness. In the optimized models, the correlation coefficients of pH, 10−pH and TA for the prediction set were 0.84, 0.82 and 0.66, respectively. The experiment results show that accurate and nondestructive prediction of lime acidity can be achieved with the grating portable NIR spectrometer and ensemble learning strategy.

Machine learning driven portable Vis-SWNIR spectrophotometer for non-destructive classification of raw tomatoes based on lycopene content

Most of the research on intact fruit spectroscopy is derivative in nature as it primarily showcase application of existing spectroscopy devices which are often proprietary in nature. The regression models developed by researchers to predict physicochemical attributes using spectra remain theoretical due to lack of mechanism to integrate the developed models back into proprietary devices. This poses challenge for commercial adaptation of this technology in commercial food quality supply chain. The present study addresses this research gap by presenting first of its kind innovative approach to classify tomatoes based on lycopene content using chemometrics-machine learning framework driven portable short-wave near infra-red (SWNIR) spectrophotometer developed by integration of open-source hardware (AS7265x multispectral chipset having wavelength range 410–940 nanometre (nm), Arduino Uno microcontroller) and software (R platform), housed in ergonomically designed and 3-dimension printed cabinet ensuring noise-free spectra acquisition. The lycopene content was observed to have strong negative correlation with wavelengths (nm) 485, 560 and 585 at ρ = – 0.65, – 0.70, – 0.70, whereas strong positive correlation with 760 nm at ρ = +0.64. Similar associations were qualitatively observed using principal component analysis. Atypical of literature, feature selection was performed based on analysis of variance and 14 wavelengths which exhibited statistically significant difference with respect to 15-days storage study (p ≤ 0.05) were selected for model development. Chemometrics-machine learning framework was used for development of optimised probabilistic and non-probabilistic models including logistic regression, Linear Discriminant Analysis (LDA), Random Forest (RF), Artificial Neural Networks (ANN) and Support Vector Machine (SVM) models using 10-fold cross validation subjected to 80–20% train-test split of the dataset. In agreement with literature, 500–750 nm wavelength range dominated the classification of lycopene content. Notably, specific wavelengths for logistic regression (560 nm), LDA (730 nm, 645 nm, 560 nm, 535 nm), RF (760 nm, 585 nm, 560 nm, 645 nm), and ANN (585 nm, 560 nm) significantly influenced outcome instances across classifiers. Accuracy obtained from confusion matrix on test dataset was used as performance metric to compare different models. Logistic regression and RF showcased accuracy of 80%, LDA and SVM at 90% while ANN outperformed all models with accuracy of 95%. This study successfully augmented technological advancement in field of spectroscopy for non-invasive quality assessment of fruit. It is recommended to conduct similar studies on other climacteric fruits for wider adoption of this technology.

A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy.

Seafood mislabeling rates of approximately 20% have been reported globally. Traditional methods for fish species identification, such as DNA analysis and polymerase chain reaction (PCR), are expensive and time-consuming, and require skilled technicians and specialized equipment. The combination of spectroscopy and machine learning presents a promising approach to overcome these challenges. In our study, we took a comprehensive approach by considering a total of 43 different fish species and employing three modes of spectroscopy: fluorescence (Fluor), and reflectance in the visible near-infrared (VNIR) and short-wave near-infrared (SWIR). To achieve higher accuracies, we developed a novel machine-learning framework, where groups of similar fish types were identified and specialized classifiers were trained for each group. The incorporation of global (single artificial intelligence for all species) and dispute classification models created a hierarchical decision process, yielding higher performances. For Fluor, VNIR, and SWIR, accuracies increased from 80%, 75%, and 49% to 83%, 81%, and 58%, respectively. Furthermore, certain species witnessed remarkable performance enhancements of up to 40% in single-mode identification. The fusion of all three spectroscopic modes further boosted the performance of the best single mode, averaged over all species, by 9%. Fish species mislabeling not only poses health-related risks due to contaminants, toxins, and allergens that could be life-threatening, but also gives rise to economic and environmental hazards and loss of nutritional benefits. Our proposed method can detect fish fraud as a real-time alternative to DNA barcoding and other standard methods. The hierarchical system of dispute models proposed in this work is a novel machine-learning tool not limited to this application, and can improve accuracy in any classification problem which contains a large number of classes.

Subnano Te Cluster in Glass for Efficient Full-Spectrum Conversion.

Broadband near-infrared (NIR) photonic materials have wide applications. Although extensive studies on rare-earth, transition-metal, and even semiconductor-activated materials have enabled the development of a rich NIR material pool, developing broadband and efficient photonic candidates covering the NIR I and II regions from 750 to 1500nm has been met with limited success. Here, it is reported that a subnano Te cluster with a characteristic configuration different from that of the ion state may fill the aforementioned gap. Further, a strategy is proposed for the in situ generation and stabilization of Te clusters by tuning the cluster evolution in glass. A novel active photonic glass embedded with a Te cluster is fabricated; it exhibits intense and broadband short-wave NIR luminescence with a central wavelength at 1030nm and a bandwidth exceeding 330nm. Interestingly, the glass exhibited a full visible-spectrum conversion ability from 300 to 800nm. The application of this unique broadband excitation feature for night vision and tissue penetration is demonstrated using a smartphone as the excitation source. These findings demonstrate a fundamental principle of cluster design in glass for creating new properties and provide a new direction for developing novel cluster-derived functional composite materials.

A new method for evaluating the penetration ability of near infrared diffuse reflectance light to fruit peel with chemometrics

In this paper, the penetration ability of near infrared (NIR) diffuse reflectance light to peel was studied with grating NIR spectrometer. 15 kinds of fruit, including 3 kinds of thin-peel non-citrus fruit, 9 kinds of medium-peel citrus and 3 kinds of thick-peel citrus were investigated. A new strategy to evaluate the NIR penetration ability was developed with a designed penetration experiment and chemometric methods. The penetration abilities of different integration time, peel thickness and wavelength bands were compared. The results show that NIR light can easily penetrate the peel of thin-peel fruit, while it is not easy to penetrate the peel of citrus due to the thick peel. Both the thickness and composition of the peel have relationship with the penetration ability. Increase of integration time can improve the penetration ability to some extent. However, as the integration time lengthens, noise interference and spectral distortion may appear in long wave near infrared (LWNIR) region. The optimal integration time is 65 ms for citrus. Besides, the penetration ability of short wave near infrared (SWNIR) light is better than that of LWNIR light, and the absorption peaks of peel are mainly located in LWNIR region, indicating that SWNIR is more suitable for fruit internal quality detection.

Portable LWNIR and SWNIR spectroscopy with pattern recognition technology for accurate and nondestructive detection of hidden mold infection in citrus

An accurate and nondestructive detection method of hidden mold infection in citrus was established based on portable near infrared diffuse reflectance spectroscopy (NIRDRS) and chemometric methods. Penetrability of NIRDRS light on the peel of Chunjian hybrid citrus was studied. The results show that NIRDRS light can penetrate the peel to a certain extent, while the penetrability of short-wave near infrared (SWNIR) was better than that of long-wave near infrared (LWNIR). The identification models of hidden mold infection were established with five pattern recognition methods combined with different wavelength bands. The results show that the identification models of LWNIR were much better than those of SWNIR. 100% identification accuracies of LWNIR were obtained with soft independent pattern classification (SIMCA), support vector machine (SVM), partial least squares discriminant analysis (PLS-DA) and the optimized pretreatment methods. In addition, the developed models were further validated by the external validation set collected one month later.

Quick Determination of Soil Quality Using Portable Spectroscopy and Efficient Multivariate Techniques

Rapid and onsite determination of the soil status and quality parameters holds a brighter potential for improving food security, and minimizing waste of the excessive application of soil amendments hence reducing environmental pollution. In this study, a pocket-sized shortwave NIR spectroscopy (740–1070 nm) and multivariate statistics were used to classify soil from different land-use types and simultaneously predict nitrogen (N), phosphorus (P), potassium (K), calcium (Ca2+), magnesium (Mg2+), and pH in Ghana. Different Algorithms. Linear discriminant analysis (LDA), support vector machine (SVM), and partial least squares algorithms (full-range partial least square, FrPLS; interval partial least squares, IPLS; synergy interval partial least squares, Si-PLS) were attempted for building a suitable classification and quantification model. The models were assessed by the classification rate, coefficient of determination (Rp2), and root mean square error of prediction (RMSEP) in the prediction set. A total of 110 soil samples from 0 to 15 cm, 15 to 30 cm, and 30 to 45 cm layers were collected from the field of different land-use cropping systems. The results obtained showed that SVM had a 98.61% classification rate of the soil from the cropping system. While Si-PLS was superior in predicting N, P, K, Mg2+, Ca2+, and pH. The performance of the Si-PLS model for N, P, K, Mg2+, Ca2+, and pH were 0.571, 0.779, 0.910, 0.778, 0.826, and 0.904 for Rp2 and 0.033%, 0.738 mg·kg−1, 0.117 cmol·kg−1, 0.654 cmol·kg−1, 3.0219 cmol·kg−1, and 0.4760 pH unit for RMSEP, respectively. The results revealed that the portable NIR spectroscopic technique could be used to measure the soil status and some quality parameters. However, further studies are needed to proof its application. This could lead to improving the yield and saving the cost of fertilizer application.

Portable NIR Spectroscopic Application for Coffee Integrity and Detection of Adulteration with Coffee Husk

Reliable and user-friendly discrimination of coffee bean integrity and quantification of adulteration in the coffee bean processing value chain would be vital for ensuring consumer trust in quality control and traceability management. In this research, a portable short-wave NIR spectroscopy coupled with chemometric data analysis was employed under different pre-treatments to develop a rapid detection technique. Different pre-processing treatments (multiplicative scatter correction; MSC, standard normal variant; SNV, first derivative; FD) together with multivariate techniques; support vector machine (SVM), linear discriminant analysis (LDA), neural network (NN), and random forest (RF) were comparatively assessed using accuracy and correlation coefficient (R) for discrimination and quantification. The results showed that the FD-LDA model had 97.78% and 100 % in both the calibration set and prediction set. In comparison, the SPA-PLS model had R = 0.9711 and 0.9897 in both the calibration set and prediction set. The outcome of this study showed portable short-wave NIR spectroscopic techniques could be used for examining the integrity of coffee.

Towards sweetness classification of orange cultivars using short-wave NIR spectroscopy

The global orange industry constantly faces new technical challenges to meet consumer demands for quality fruits. Instead of traditional subjective fruit quality assessment methods, the interest in the horticulture industry has increased in objective, quantitative, and non-destructive assessment methods. Oranges have a thick peel which makes their non-destructive quality assessment challenging. This paper evaluates the potential of short-wave NIR spectroscopy and direct sweetness classification approach for Pakistani cultivars of orange, i.e., Red-Blood, Mosambi, and Succari. The correlation between quality indices, i.e., Brix, titratable acidity (TA), Brix: TA and BrimA (Brix minus acids), sensory assessment of the fruit, and short-wave NIR spectra, is analysed. Mix cultivar oranges are classified as sweet, mixed, and acidic based on short-wave NIR spectra. Short-wave NIR spectral data were obtained using the industry standard F-750 fruit quality meter (310–1100 nm). Reference Brix and TA measurements were taken using standard destructive testing methods. Reference taste labels i.e., sweet, mix, and acidic, were acquired through sensory evaluation of samples. For indirect fruit classification, partial least squares regression models were developed for Brix, TA, Brix: TA, and BrimA estimation with a correlation coefficient of 0.57, 0.73, 0.66, and 0.55, respectively, on independent test data. The ensemble classifier achieved 81.03% accuracy for three classes (sweet, mixed, and acidic) classification on independent test data for direct fruit classification. A good correlation between NIR spectra and sensory assessment is observed as compared to quality indices. A direct classification approach is more suitable for a machine-learning-based orange sweetness classification using NIR spectroscopy than the estimation of quality indices.

Hyperspectral image-based measurement of total flavonoid content of leaf-use Ginkgo biloba L.

Ginkgo biloba leaf is a kind of food raw material with high concentration of total flavonoid content (TFC). Since conventional measurements of TFC are time-consuming and destructive, the approach of hyperspectral imaging-based prediction was investigated in this study. Hyperspectral images were collected in visual near-infrared (VIS-NIR) and short-wave near-infrared (SW-NIR). Initial comparative analysis on the full spans of the two wavelengths using partial least squares regression (PLSR) and support vector regression (SVR) showed that the TFC of the leaves could be barely captured only when PLSR working on short-wave infrared, withRp2 and RMSEp on testing set being respectively 0.5496 and 0.6384 mg/g. To further improve prediction performance meeting industrial requirements, another comparative study on feature selection was conducted to fine-tune the PLSR on SW-NIR using a genetic algorithm (GA) and successive projections algorithm (SPA). Results showed that GA-PLSR with 50 characteristic wavelengths mostly from the ranges of 1100-1200 nm and 1400-1500 nm had a significant improvement in performance, giving 0.8482 and 0.2967 mg/g for Rp2 and RMSEp, respectively. Therefore, an approach, GA-PLSR modeling on SW-NIR hyperspectral images, was established for the ginkgo leaves industry to rapidly and non-invasively predict the leaf concentration from the total flavonoid of ginkgo biloba seedling leaf.

Reliable classification of nutmeg fruits from some geographical origins using SWNIR spectroscopy associated with PLS-DA and SVM methods: Case Study in Aceh Province, Indonesia

Essential oils of nutmeg (Myristica fragrans) can have different qualities due to the soil and industrial process, as Indonesia’s main nutmeg production areas are enormous, especially in Aceh province. The lack of information on detecting chemical constituents and classifying nutmeg from Aceh province makes the price of nutmeg from Aceh unable to compete. Therefore, this study aims to reliably classify nutmeg fruits of some geographical origins in Aceh Province using shortwave near-infrared (SWNIR) spectroscopy associated with partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM). The SWNIR used in this study has a wavelength of 381 to 1065 nm at a resolution of 3 nm and a diffuse reflectance mode. The fruit comes from four geographical areas of origin in the province of Aceh. A total of forty SWNIR spectral data were further classified using two algorithms, namely PLS-DA and SVM. The results show that SWNIR can correctly classify nutmeg from some sub-districts in Aceh province with the help of the PLS-DA and SVM algorithms. From this study, the following SVM algorithm is recommended to be used to classify nutmeg from the province of Aceh with an outstanding level of precision.

Detection of Early Bruises in Honey Peaches Using Shortwave Infrared Hyperspectral Imaging

Honey peaches can bruise during harvesting, handling, storage, transportation, and distribution. In this study, the spectral range used was 400–1100 nm, and we extracted the RGB and HSI color space characteristics of the images. After principal component analysis (PCA) of the original data, the gray histogram features of the PC1 images were extracted. Partial least squares qualitative discriminant analysis (PLS-DA) and extreme learning machine (ELM) discriminant models were established. Among the 38 color features, the PLS-DA and ELM models had a high rate of misclassification, and the best classification accuracy was 74.29%. When extracting the spectral information of the bruised sample to build the model, the highest classification accuracy was 92.86% for the 176 characteristic wavelength points of the full band. In contrast, only 40 wavelength bands were used after selecting the genetic algorithm’s valid information. The classification accuracy of the PLS-DA model was 100%, which is because the softening and browning of the peach was not apparent after early bruising. However, the changes in the tissue’s thermal properties caused by internal defects are expressed in the internal spectrum. Therefore, the shortwave NIR hyperspectral imaging technique’s spectral information can detect the early bruising of peaches.

Optical Multisensor System Based on Lanthanide(III) Complexes as Near-Infrared Light Sources for Analysis of Milk

Optical multisensor systems are easy-to-use and inexpensive analytical devices. In this work, we propose an optical multisensor system based on the luminescence of Nd(III) and Yb(III) complexes in the near-infrared (NIR) spectral region. The observed emission bands play the role of secondary light sources for further analysis of milk—for the determination of fat content and for the recognition of adulteration. The samples for analysis were prepared by putting a drop of milk upon a thin glass covering the powdered mixture of lanthanide complexes, which were excited by a light-emitting diode (LED) in the ultraviolet region (the maximum intensity at 365 nm). The diffuse-reflectance spectra of samples were acquired in the short-wave NIR range 750–1100 nm using a portable NIR spectrometer. The developed optical system was tested using two sets of milk samples with varying concentration levels of fat and added urea. The obtained spectral data were analyzed using a number of multivariate prediction and classification methods of chemometrics and the results were statistically compared. The regression and classification model performances achieved in this proof-of-concept study illustrate the feasibility of the optical multisensor analysis based on luminescent light sources in the short-wave NIR range, in particular, for their application in the dairy.

Visible and Near-Infrared Spectroscopy Combined With Bayes Classifier Based on Wavelength Model Optimization Applied to Wine Multibrand Identification.

The identification of high-quality wine brands can avoid adulteration and fraud and protect the rights and interests of producers and consumers. Since the main components of wine are roughly the same, the characteristic components that can distinguish wine brands are usually trace amounts and not unique. The conventional quantitative detection method for brand identification is complicated and difficult. The naive Bayes (NB) classifier is an algorithm based on probability distribution, which is simple and particularly suitable for multiclass discriminant analysis. However, the absorbance probability between spectral wavelengths is not necessarily strongly independent, which limits the application of Bayes method in spectral pattern recognition. This research proposed a Bayes classifier algorithm based on wavelength optimization. First, a large-scale wavelength screening for equidistant combination (EC) was performed, and then wavelength step-by-step phase-out (WSP) was carried out to reduce the correlation between wavelengths and improve the accuracy of Bayes discrimination. The proposed EC-WSP-Bayes method was applied to the 5-category discriminant analysis of wine brand identification based on visible and near-infrared (Vis-NIR) spectroscopy. Among them, four types of wine brands were collected from regular sales channels as identification brands. The fifth type of samples was composed of 21 other commercial brand wines and home-brewed wines from various sources, as the interference brand. The optimal EC-WSP-Bayes model was selected, the corresponding wavelength combination was 404, 600, 992, 2,070, 2,266, and 2,462 nm located in the visible light, shortwave NIR, and combination frequency regions. In modeling and independent validation, the total recognition accuracy rate (RARTotal) reached 98.1 and 97.6%, respectively. The technology is quick and easy, which is of great significance to regulate the alcohol market. The proposed model of less-wavelength and high-efficiency (N = 6) can provide a valuable reference for small special instruments. The proposed integrated chemometric method can reduce the correlation between wavelengths, improve the recognition accuracy, and improve the applicability of the Bayesian method.