Portable Near-infrared Spectroscopy Research Articles

Non-invasive discrimination of roasted and unroasted cocoa bean shell of cocoa clones in Ghana and quantification of nutritional and bioactive components: a chemometric approach

Cocoa bean shell (CBS) remains a commonly produced by-product of cocoa bean processing. It is usually obtained from fermented and dried cocoa beans that are roasted. The study investigated the potential use of Near-infrared spectroscopy (NIRS) analysis for discriminating roasted and unroasted CBS among cocoa clones and quantifying some nutritional and bioactive components in Ghana. Five clones, comprising four important seed gardens clones used across West Africa and one criollo were evaluated. Cocoa beans from the different clones (T60/887, VENC 4, MO 20, PA 150 and T60/887 × POUND 7) were divided into two parts, with one part roasted at a temperature of 120 °C for 50 min while the other part was kept unroasted. The CBSs were milled and passed through a 425 μm pore-sized sieve to obtain the powder. A handheld portable NIRS was used to scan the CBS powder in Ziplock bags. The nutritional and bioactive characterisation was carried out using official methods. NIRS discriminated the various clones of roasted and unroasted CBS. Carbohydrate was the predominant macronutrient, and ash content ranged from 5.25 to 8.24%. The CBS was high in potassium (2382–3144 mg/100 g) and low in sodium (25.67–51.33 mg/100 g). Total flavonoids and phenolics ranged from 8.61 to 40.71 mgQE/g and 6.34–12.25 mgGAE/g, respectively, for the roasted and unroasted CBS. To ensure better differentiation of cocoa beans from different clones using NIRS, incorporating roasting as a processing parameter is recommended.

Authentication of the shelf-life of NPH injectable insulins samples using NIR and MS spectral fingerprints

Insulin is essential for regulating glucose levels, and its hormonal deficiency leads to diabetes. Treatment involves the use of different types of insulin tailored to individual needs. Properly stored and transported Neutral Protamine Hagedorn (NPH) insulin remains effective and stable, highlighting the importance of quality control to ensure its efficacy and patient safety. This study aims to develop an analytical method to evaluate the shelf-life of NPH injectable insulin samples while preserving packaging integrity. The approach consisted of analyzing intact insulin samples, with and without expired shelf-life dates, using near-infrared spectroscopy (NIR) obtained from two instruments: a benchtop and a portable device. These measurements were coupled with chemometric methods, specifically single-class classification algorithms. The classifiers considered were Data Driven-Soft Independent Modeling of Class Analogy (DD-SIMCA) and One-Class Partial Least Squares (OC-PLS). For benchtop NIR, the DD-SIMCA method, with Savitzky-Golay smoothing preprocessing (41-point window) and baseline offset (BO), achieved excellent results, reaching 100% sensitivity, specificity, and efficiency in both training and test sets. As for portable NIR, the most effective methods were DD-SIMCA and OC-PLS, using datasets preprocessed with Savitzky-Golay smoothing with first derivative, second-order polynomial (SGD-1st-2nd), and 9-point windows combined with Standard Normal Variate (SNV), and Savitzky-Golay smoothing filters (SGS) using second-order polynomial and 7-point windows combined with baseline offset (BO) and Multiplicative Scattering Correction (MSC). The results were corroborated by mass spectrometry, correlating the structural changes of degraded insulin with NIR data. The proposed methodology is a promising tool for non-destructive, rapid, and cost-effective authentication of the preservation state of NPH insulin, aligning with green chemistry principles and allowing for in-situ evaluation, thereby preserving medication integrity.

Handheld Near-Infrared Spectroscopy for Undried Forage Quality Estimation.

This study investigates the efficacy of handheld Near-Infrared Spectroscopy (NIRS) devices for in-field estimation of forage quality using undried samples. The objective is to assess the precision and accuracy of multiple handheld NIRS instruments-NeoSpectra, TrinamiX, and AgroCares-when evaluating key forage quality metrics such as Crude Protein (CP), Neutral Detergent Fiber (aNDF), Acid Detergent Fiber (ADF), Acid Detergent Lignin (ADL), in vitro Total Digestibility (IVTD)and Neutral Detergent Fiber Digestibility (NDFD). Samples were collected from silage bunkers across 111 farms in New York State and scanned using different methods (static, moving, and turntable). The results demonstrate that dynamic scanning patterns (moving and turntable) enhance the predictive accuracy of the models compared to static scans. Fiber constituents (ADF, aNDF) and Crude Protein (CP) show higher robustness and minimal impact from water interference, maintaining similar R2 values as dried samples. Conversely, IVTD, NDFD, and ADL are adversely affected by water content, resulting in lower R2 values. This study underscores the importance of understanding the water effects on undried forage, as water's high absorption bands at 1400 and 1900 nm introduce significant spectral interference. Further investigation into the PLSR loading factors is necessary to mitigate these effects. The findings suggest that, while handheld NIRS devices hold promise for rapid, on-site forage quality assessment, careful consideration of scanning methodology is crucial for accurate prediction models. This research contributes valuable insights for optimizing the use of portable NIRS technology in forage analysis, enhancing feed utilization efficiency, and supporting sustainable dairy farming practices.

Glycogen Quantification and Gender Identification in Di-, Tri-, and Tetraploid Crassostrea gigas Using Portable Near-Infrared Spectroscopy.

Near-infrared spectroscopy (NIR) has become an essential tool for non-destructive analysis in various fields, including aquaculture. This study presents a pioneering application of portable NIR spectrometers to analyze glycogen content in the gonadal tissues of the Pacific oyster (Crassostrea gigas), marking the first instance of developing quantitative models for glycogen in tetraploid C. gigas. The research also provides a comparative analysis with models for diploid and triploid oysters, underscoring the innovative use of portable NIR technology in aquaculture. Two portable NIR spectrometers were employed: the Micro NIR 1700 (908-1676 nm) and the Micro PHAZIR RX (1624-2460 nm). Near-infrared spectra were acquired from the gonadal tissues of diploid, triploid, and tetraploid C. gigas. Quantitative models for glycogen content were developed and validated using cross-validation methods. Additionally, qualitative models for different ploidies and genders were established. For the Micro NIR 1700, the cross-validation correlation coefficients (Rcv) and cross-validation relative predictive errors (RPDcv) for glycogen were 0.949 and 3.191 for diploids, 0.915 and 2.498 for triploids, and 0.902 and 2.310 for tetraploids. The Micro PHAZIR RX achieved Rcv and RPDcv values of 0.781 and 2.240 for diploids, 0.839 and 2.504 for triploids, and 0.717 and 1.851 for tetraploids. The Micro NIR 1700 demonstrated superior quantitative performance, with RPD values exceeding 2, indicating its effectiveness in predicting glycogen content across different ploidy levels. Qualitative models showed a performance index of 91.6 for diploid and 95 for tetraploid genders using the Micro NIR 1700, while the Micro PHAZIR RX achieved correct identification rates of 99.79% and 100% for diploid and tetraploid genders, respectively. However, differentiation of ploidies was less successful with both instruments. This study's originality lies in establishing the first quantitative models for glycogen content in tetraploid C. gigas using portable NIR spectrometers, highlighting the significant advancements in non-destructive glycogen analysis. The applicability of these findings is substantial for oyster breeding programs focused on enhancing meat quality traits. These models provide a valuable phenotyping tool for selecting oysters with optimal glycogen content, demonstrating the practical utility of portable NIR technology in aquaculture.

Portable near-infrared (NIR) spectroscopy and multivariate calibration for reliable quality control of maize and sorghum grain chemical composition

Portable near-infrared (NIR) spectroscopy combined with multivariate calibration was used to determine the chemical composition of maize and sorghum grains of different genotypes cultivated in Brazil. Partial least squares (PLS) models were built, and the maize models exhibited R2 > 0.90 for all constituents except starch. For sorghum, the R2 was > 0.8 for all componentes. The Residual Prediction to Deviation (RPD) values and the Range Error Ratio (RER) values for all maize and sorghum calibration models exceeded 2.5 and 10, respectively, indicating the models’ reliability for routine laboratory implementation. No significant differences were found between the results obtained by the PLS models and the reference methods for any analyzed constituents in the maize and sorghum grains. These results demonstrate that the MicroNIR instrument is a viable tool for quality control of chemical composition in maize and sorghum grain samples. It offers the advantages of speed, non-destructiveness, and being a green alternative to standard methods.

Application of Portable Near-Infrared Instrument for Analysis of Spirulina platensis Aqueous Extracts

Spirulina platensis microalga has become recognized as a promising source of highly nutritious food components to feed the growing global population. Because of its high protein content, abundance of essential amino acids, and excellent digestion, it is employed in human nutrition and there is growing interest in analyzing bioactive compound present in Spirulina platensis microalga. In this work, a portable near-infrared (NIR) spectrometer was used for the monitoring of physicochemical properties (extraction yield (EY), total polyphenols concentration (TPC), total proteins concentration (TP), antioxidant activity measured by (i) the DPPH method (DPPH) and (ii) FRAP method (FRAP)) of Spirulina platensis aqueous extracts. The ultrasound-assisted aqueous extraction (ultrasonic bath with an ultrasound frequency of 35 kHz) of bioactive molecules from Spirulina platensis was performed throughout 30 independent experiments. NIR spectra were recorded in the wavelength range of 900–1700 nm. Raw NIR spectra were subjected to the physicochemical properties applying Principal Component Analysis (PCA), partial least square (PLS), and artificial neural network (ANN) modeling. Results show that ANN models developed for the prediction of TPC and DPPH can be utilized for extraction process control (RER > 10), while the other three models can be employed for screening (RER > 4). Generally, the obtained results indicate significant potential for using portable NIR spectroscopy for the analysis of Spirulina platensis aqueous extracts.

Assessment of Peanut Protein Powder Quality by Near-Infrared Spectroscopy and Generalized Regression Neural Network-Based Approach.

The global demand for protein is on an upward trajectory, and peanut protein powder has emerged as a significant player, owing to its affordability and high quality, with great future market potential. However, the industry currently lacks efficient methods for rapid quality testing. This research paper addressed this gap by introducing a portable device with employed near-infrared spectroscopy (NIR) to quickly assess the quality of peanut protein powder. The principal component analysis (PCA), partial least squares (PLS), and generalized regression neural network (GRNN) methods were used to construct the model to further enhance the accuracy and efficiency of the device. The results demonstrated that the newly established NIR method with PLS and GRNN analysis simultaneously predicted the fat, protein, and moisture of peanut protein powder. The GRNN model showed better predictive performance than the PLS model, the correlation coefficient in calibration (Rcal) of the fat, the protein, and the moisture of peanut protein powder were 0.995, 0.990, and 0.990, respectively, and the residual prediction deviation (RPD) were 10.82, 10.03, and 8.41, respectively. The findings unveiled that the portable NIR spectroscopic equipment combined with the GRNN method achieved rapid quantitative analysis of peanut protein powder. This advancement holds a significant application of this device for the industry, potentially revolutionizing quality testing procedures and ensuring the consistent delivery of high-quality products to fulfil consumer desires.

Rapid assessment of vanilla (Vanilla planifolia) quality parameters using portable near-infrared spectroscopy combined with random forest

The determination of moisture and vanillin content significantly influences the quality of vanilla. Currently, conventional chemical methods employed for assessing these parameters are time-consuming, involve complex sample preparation, are expensive, and environmentally unfriendly due to the use of chemical solutions. Portable Near-Infrared (NIR) spectroscopy emerges as a promising alternative, characterized by smaller dimensions and lower costs. This study investigates the performance of two portable NIR spectrometers with distinct wavelengths at 740–1070 nm and 1350–2550 nm, in conjunction with Random Forest (RF) and Partial Least Square (PLS) regression, and preprocessing techniques including min-max normalization, 1st derivative, standard normal variate (SNV), multiplicative scatter correction (MSC), 1st derivative + SNV, and 1st derivative + MSC for predicting moisture and vanillin content. At the wavelength range of 1350–2550 nm, RF coupled with 1st derivative produced the best moisture content prediction model with an R2 of 0.971, and RF paired with 1st derivative+SNV yielded the best vanillin content prediction with an R2 of 0.983. This work highlights that the integration of portable NIR and RF allows for rapid and non-destructive detection of moisture and vanillin content. This methodology provides a novel regression method for predicting vanilla qualities.

Differentiation between fresh and frozen-thawed mackerel fish using low-cost portable near infrared spectrometry devices

Mislabelling frozen-thawed fish fillets as fresh is one of the most important fraudulent practices during commercialisation. This study aimed to determine the ability of two portable miniaturised low-cost near-infrared spectroscopy (NIR) devices intended for consumers to discriminate between fresh and thawed mackerel (submitted to one or two frozen-thawed cycles) and between different freezing systems. The effect of different fish seasonal characteristics on the performance of the model was also evaluated. The low-cost NIR devices were able to discriminate between fresh and thawed samples with a classification rate of 90.3% and 94.1% and the freezing system to which they were submitted with a classification rate of 91.2% and 89.7%. These findings suggest that low-cost portable NIR spectroscopy can be a valuable tool for detecting mislabeled frozen-thawed products sold as fresh, providing consumers with a rapid and affordable method for fraud detection.

Rapid quantification models for assessing melamine adulteration in sport nutrition supplements via benchtop and portable NIRS instruments

Sport nutrition supplements (SNS) are vulnerable to adulteration with melamine, artificially augmenting their protein content as determined by conventional assay methodologies. Vibrational spectroscopy techniques are suitable for the detection of adulteration because they allow rapid analysis, require minimal sample preparation, and can perform numerous analyses in a short time. The aim of this study was to develop rapid quantification models for the determination of melamine adulteration in a variety of SNS matrices using NIRS (near-infrared spectroscopy) in combination with multivariate data processing. Moreover, a comparison of benchtop and portable NIR instruments was carried out. Employing a stepwise approach involving OPLS-DA and PLS analysis, matrix discrimination and prediction ability were investigated. The benchtop instrument effectively discriminated among matrices (R2Y = 0.964, Q2 = 0.933), while the portable device, although showing a slightly altered pattern, maintained favorable discrimination capability (R2Y = 0.966, Q2 = 0.931). The quantitative PLS models for each SNS matrix exhibited comparable statistical indicators for both instruments with reasonable errors for melamine content estimation and prediction (RMSEE: 0.3–2.4 %, RMSEP: 0.98–2.99 %). Higher estimation and prediction errors were observed for protein-containing samples in both acquisition modes, probably due to the tendency of protein agglomeration and adhesion to different surfaces, which affects the homogeneity of the powder. Despite data loss due to the narrower spectral range and lower resolution of the portable instrument, all models were found to be suitable for predicting melamine content in sport nutrition supplements.

Effect of Sample Presentation on the Classification of Black Soldier Fly Larvae Using Near-Infrared Spectroscopy

Black soldier fly larvae (BSFL) (Hermetia illucens) reared on food waste streams are considered a sustainable source of protein in feed livestock diets. Recently, portable near-infrared spectroscopy (NIR) instruments have been assessed to monitor the consistency and quality of food waste streams used to feed black soldier fly larvae. During the application of NIR spectroscopy, sample presentation (e.g., drying, processing, particle size) plays an important role in the accuracy of the models developed (quantitative or qualitative analysis). The objective of this study was to evaluate the effect of sample presentation (number of larvae used during the scanning of BSFL) on the accuracy of classification models developed to trace the food waste stream (e.g., supermarket of childcare) used to feed the larvae. BSFL samples were sourced from two waste streams and scanned as half, 1, 2, or 3 larvae using an NIR portable instrument (MicroNIR, Viavi, Milpitas, California, USA). Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to analyze the NIR data and to classify the samples according to the waste stream. The main differences in the NIR spectra of the BSFL samples associated with the number of larvae scanned were observed around 1200 nm, mainly associated with the C-H overtones (lipids). The classification results showed that high classification rates (>93%) were obtained regardless of the number of larvae scanned, ranging from 93% (using 0.5 larvae) to 100% (using 1, 2, or 3 larvae samples). Overall, the number of larvae scanned had minimal to no effect on the accuracy of the LDA classification models. The present study demonstrated that a portable NIR instrument can be suitable for an initial rapid classification or determination of the origin of the waste stream used to feed the BSFL.

Development of novel portable NIR spectroscopy process analytical technology (PAT) tool for monitoring the transition of ibuprofen to ibuprofen sodium during wet granulation process

The aim of this research was to develop a process analytical technology (PAT) tool for monitoring the transformation of the active ingredient ibuprofen into the fast-dissolving salt ibuprofen sodium during the wet granulation process. Two near-infrared (NIR) spectrophotometers, portable and benchtop spectrophotometer, were compared. During the analysis with the built models, both demonstrated comparable accuracy and precision (R2X = 0.995, R2Y = 0.927, Q2 = 0.995, and R2X = 0.990, R2Y = 0.948, Q2 = 0.992, respectively). Considering the applicability, a model based on the portable NIR spectroscopic data was chosen for further development and application as a PAT tool for monitoring different steps during the wet granulation process. The evaluation of the model's predictive capability involved analyzing laboratory trial batches with varying amounts of sodium carbonate, resulting in different concentrations of ibuprofen sodium at the end of the wet granulation process. Subsequently, tablets were manufactured from each trial batch, followed by dissolution analysis. The dissolution rate assays were in good agreement with the NIR-predicted concentrations of ibuprofen sodium at the end of the wet granulation process. Based on the results, the proposed model provides an excellent tool to monitor the ibuprofen acid-salt transformation, to determine the end-point of the reaction, and to efficiently control the wet granulation process.

Developing multifruit global near‐infrared model to predict dry matter based on just‐in‐time modeling

AbstractModeling near‐infrared (NIR) spectral data to predict fresh fruit properties is a challenging task. The difficulty lies in creating generalized models that can work on fruits of different cultivars, seasons, and even multiple commodities of fruit. Due to intrinsic differences in spectral properties, NIR models often fail in testing, resulting in high bias and prediction errors. One current solution for achieving generalized models is to use large calibration sets measured over multiple cultivars and harvest seasons. However, current practice primarily focuses on calibration sets for single fruit commodities, disregarding the rich information available from other fruit commodities. This study aims to demonstrate the potential of locally weighted partial least‐squares an example of just‐in‐time (JIT) modeling to develop real‐time models based on calibration sets consisting of multiple fruit commodities. The study also explores JIT modeling for leveraging relevant information from other fruit commodities or adapting the model based on new samples. The application demonstrated here predicts the dry matter in fresh fruit using portable NIR spectroscopy. The results show that JIT modeling is particularly effective for multiple fruit commodities in a single calibration set. The JIT models achieved a root mean squared error of prediction (RMSEP) of 0.69% fresh weight (FW), while the traditional partial least squares (PLS) modeling RMSEP was 0.93% FW. JIT modeling can be particularly beneficial when the user has multiple fruit datasets and wants to combine them into a single dataset to utilize all the relevant information available.

The ability of a handheld near-infrared spectrometer to do a rapid quality assessment of bovine colostrum, including the immunoglobulin G concentration

Portable infrared-based instruments have made significant contributions in different research fields. Within the dairy supply chain, for example, most of portable devices are based on near-infrared spectroscopy (NIRS) and are nowadays an important support for farmers and operators of the dairy sector, allowing fast decision-making, particularly for feed and milk quality evaluation and animal health and welfare monitoring. The affordability, portability, and ease of use of these innovative devices have been pivotal factors for the implementation in dairy farms. In fact, pocket-sized devices enable non-expert users to perform quick, low cost and non-destructive analysis on various samples without complex preparation. As bovine colostrum (BC) quality is mostly given by the Ig G (IgG) level, evaluating the ability of portable NIRS tools to measure antibodies concentration is advisable. In this study we used the wireless device SCiO manufactured by Consumer Physics Inc. (Tel Aviv, Israel) to collect BC spectra and then attempt to predict IgG concentration and gross and fine composition in individual samples collected as soon as possible after calving (<6 h) in primiparous and pluriparous Holstein cows farmed in 9 Italian farms. Chemometric analyses revealed that SCiO has promising predictive performance for colostral IgG concentration, total Ig concentration, fat, and AA (R2CV ≥ 0.75). Excellent accuracy was observed for dry matter, protein, and S prediction in cross-validation and good prediction ability in external validation (R2CV ≥ 0.93; R2V ≥ 0.82). Nonetheless, SCiO's ability to discriminate between good- and low-quality samples was satisfactory. The affordable cost, the accurate predictions, and the user-friendly design coupled with the increased interest in colostrum quality within the dairy sector may boost the collection of extensive BC data for management and genetic purposes in the near future.

Real-time and non-destructive control of the freshness and viability of live mussels through portable near-infrared spectroscopy

This study introduces the application of a novel, rapid, and non-destructive method employing portable near-infrared (NIR) spectroscopy to assess the freshness and viability of live mussels throughout their shelf-life. NIR spectra ranging from 908 to 1676 nm were collected from 150 Mediterranean mussels (Mytilus galloprovincialis, L.) over a 10-day storage period. Simultaneously, measurements of weight loss, intravalvular liquid pH, and nonprotein nitrogen levels were taken. A predictive model was established by correlating the multivariate information derived from NIR spectral data with days of storage under refrigerated conditions using orthogonal partial least squares regression (OPLSR) analysis. While physical and chemical parameters (except for gradual weight loss due to water leakage) showed no distinct trends throughout storage, the fourth derivative-preprocessed NIR spectra enabled the construction of an OPLSR model that, following cross-validation, exhibited a correlation coefficient of 0.86 and, following external validation with new mussel samples, an average accuracy (root mean square error of prediction) of just 1.3 days. The predictive power of the model was primarily attributed to specific NIR wavelengths associated with key chemical features, including unsaturated fatty acids, nitrogen compounds, water content, glycerol, and ATP-related compounds that, collectively, constituted a distinctive fingerprint for forecasting mussel storage duration. The performance of the tool, along with its environmentally friendly, non-invasive, real-time, and cost-effective characteristics, align with industry control procedures and inspection needs, allowing effective freshness and viability assessment of live mussels across the food supply chain.

Rapid and non-destructive quality estimation of cinchona, andrographis paniculata, and black pepper using a portable NIR spectroscopy measuring device

Quality assessment of natural products are mostly done by laboratory methods which are invasive, cost-intensive, with low-throughput and prone to human error. Near infrared (NIR) spectroscopy have emerged as a very useful technique as a rapid non-destructive detection and quality estimation tool in identifying chemical fingerprint sensitive to the vibrational modes in chemical bonds like O-H, C-H and N-H.In this work, a reasonably low-cost and portable NIR spectrometer was developed with a few imported internal components, together with compatible software for its use, like the graphical user interface (GUI) and calibration program. Quinine in Cinchona (Cinchona L) bark, andrographolides in Andrographis paniculata and piperine in black pepper were estimated and the coefficient of determination (R2) of 0.93, 0.94, and 0.90 was obtained respectively for the regression analysis. The model for cinchona, andrographolides, and piperine gave residual prediction deviation (RPDs) of 4.25, 4.21 and 3.0, range error ratio (RERs) of 14.59, 14.42, 10.32 and the ratio between the standard error of calibration (SEC) and the standard error of prediction (SEP) i.e. SEP/SEC of 1.18, 0.21 and 0.92 respectively. The derived partial least squares (PLS) regression models fulfil the requirements of AACC Method 39–00 (AACC in AACC Method, 39–00:15, 1999) used for screening (RPD ≥ 2.5), as per knowledge of the authors. The device can be used for other molecular markers in plants and their byproducts.

Detecting Respiratory Viruses Using a Portable NIR Spectrometer-A Preliminary Exploration with a Data Driven Approach.

Respiratory viruses' detection is vitally important in coping with pandemics such as COVID-19. Conventional methods typically require laboratory-based, high-cost equipment. An emerging alternative method is Near-Infrared (NIR) spectroscopy, especially a portable one of the type that has the benefits of low cost, portability, rapidity, ease of use, and mass deployability in both clinical and field settings. One obstacle to its effective application lies in its common limitations, which include relatively low specificity and general quality. Characteristically, the spectra curves show an interweaving feature for the virus-present and virus-absent samples. This then provokes the idea of using machine learning methods to overcome the difficulty. While a subsequent obstacle coincides with the fact that a direct deployment of the machine learning approaches leads to inadequate accuracy of the modelling results. This paper presents a data-driven study on the detection of two common respiratory viruses, the respiratory syncytial virus (RSV) and the Sendai virus (SEV), using a portable NIR spectrometer supported by a machine learning solution enhanced by an algorithm of variable selection via the Variable Importance in Projection (VIP) scores and its Quantile value, along with variable truncation processing, to overcome the obstacles to a certain extent. We conducted extensive experiments with the aid of the specifically developed algorithm of variable selection, using a total of four datasets, achieving classification accuracy of: (1) 0.88, 0.94, and 0.93 for RSV, SEV, and RSV + SEV, respectively, averaged over multiple runs, for the neural network modelling of taking in turn 3 sessions of data for training and the remaining one session of an 'unknown' dataset for testing. (2) the average accuracy of 0.94 (RSV), 0.97 (SEV), and 0.97 (RSV + SEV) for model validation and 0.90 (RSV), 0.93 (SEV), and 0.91 (RSV + SEV) for model testing, using two of the datasets for model training, one for model validation and the other for model testing. These results demonstrate the feasibility of using portable NIR spectroscopy coupled with machine learning to detect respiratory viruses with good accuracy, and the approach could be a viable solution for population screening.

Determining the geographical origin and glycogen content of oysters using portable near-infrared spectroscopy: Comparison of classification and regression approaches

Oysters are extensively cultivated worldwide. However, significant variations in chemical composition, quality, and price exist between oysters from different geographical origins. This study employed portable near-infrared spectroscopy in conjunction with chemometric analysis to determine the geographical origin and glycogen content of oysters. Pretreatment methods (multiplicative scattering correction, first derivative, and second derivative) were used to preprocess the raw spectra. Partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares discriminant analysis (OPLS-DA), and support vector machine (SVM) were then adopted to establish the qualitative models. Partial least squares regression (PLSR) and support vector machine regression (SVMR) were compared for predicting the glycogen content. The results revealed that the PLS-DA, OPLS-DA, and SVM models classified the geographical origin of oysters with 100% accuracy. For quantitative analysis, the regression equations displayed high predictive ability. The SVMR model was superior to the PLSR model for glycogen content prediction, with a coefficient of determination of prediction (R2P) of 0.9253 and a residual prediction deviation (RPD) of 3.62. Therefore, the proposed approach is suitable for the accurate and environmentally friendly determination of the geographical origin and glycogen content of oysters, thus representing an attractive alternative method for the traceability supervision and quantitative analysis of seafood products.

Discrimination of baru oil adulteration with soy oil using portable near infrared spectroscopy, Fourier-transform infrared spectroscopy and Partial Least-Squares Discriminant

Considered an important source of bioactive compounds and unsaturated fatty acids, Baru oil comes from the Brazilian Cerrado ecoregion and is sold with high value. Methods to detect tampering to certify the authenticity of this type of product are in great demand for quality control. Herein, this study used partial least squares for discriminant analysis (PLS-DA) combined with Fourier-transform infrared spectroscopy (FTIR) and portable near-infrared spectroscopy (NIR) for the detection of adulteration in Baru oil, using soybean oil as an adulterant. Adulteration concentrations were 10, 30, 50, 60, and 80% for analysis by NIR and 10, 30, 50, 60, 70, and 90% for FTIR. After the spectral range selection and pre-processing steps, discrimination models showed good discrimination, and the efficiency was confirmed from the figures of merit, which highlighted percentages above 99% with both techniques used. Anomalous samples were identified in discrimination models obtained with a portable NIR spectrometer in the outlier detection step. The methodology evaluated is direct, fast, and non-destructive, in which the PLS-DA models obtained from the sets of spectra by FTIR presented superior results than those evaluated with the portable NIR spectrometer; however, the latter was also efficient in discriminating as adulterated, reinforcing the feasibility of using spectroscopic techniques and chemometric tools.

Quantification of caffeine and catechins and evaluation of bitterness and astringency of Pu-erh ripen tea based on portable near-infrared spectroscopy

Rapid and accurate assaying catechins, caffeine, bitterness and astringency prediction are integral to quality assessment and control of Pu-erh ripen tea (PRT). The traditional experimental procedures for content quantification and sensory evaluation are time-consuming and lab-dependent, while near-infrared (NIR) technology has the potential to address this issue. In this work, an efficient and environment-friendly approach was established for caffeine and catechins prediction, and bitterness and astringency evaluation of PRT, using a portable NIR spectrometer coupled with chemometrics. A total of 100 PRT samples were collected for analysis and seven different spectral preprocessing methods and two variable selection algorithms were employed to improve the prediction performance of the partial least squares regression (PLSR) model. The optimized models achieved satisfactory results for the prediction of caffeine and catechins (catechin, catechin gallate, gallocatechin, epicatechin gallate, epigallocatechin, and epigallocatechin gallate), as well as bitterness and astringency, with the correlation coefficient of the prediction set (Rp) above 0.9, and residual prediction deviation (RPD) over 2.5. This research offers an alternative for portable and rapid quantification of caffeine and eight catechins and evaluation of bitterness and astringency of PRT.