NIR Technology Research Articles

The Role of Near-Infrared Spectroscopy in Food Quality Assurance: A Review of the Past Two Decades

During food quality control, NIR technology enables the rapid and non-destructive determination of the typical quality characteristics of food categories, their origin, and the detection of potential counterfeits. Over the past 20 years, the NIR results for a variety of food groups—including meat and meat products, milk and milk products, baked goods, pasta, honey, vegetables, fruits, and luxury items like coffee, tea, and chocolate—have been compiled. This review aims to give a broad overview of the NIRS processes that have been used thus far to assist researchers employing non-destructive techniques in comparing their findings with earlier data and determining new research directions.

In-house validation of a visible and near infrared spectroscopy non-targeted method to support panel test of virgin olive oils

In this study, an in-house validation of Visible and Near Infrared Spectroscopy was performed to distinguish between extra virgin olive oil (EVOO) and virgin olive oil (VOO). A total of 161 samples of olive oil of three different categories (EVOO, VOO and lampante (LOO)) were analysed by transflectance using a monochromator instrument. One-class models were initially developed using Partial Least Squares (PLS) Density Modelling to characterize EVOO and VOO category. Once the LOO samples were discriminated, linear and non-linear discriminant models were built to classify EVOO and VOO. Different data pre-treatments and variable selection algorithms were evaluated to establish the best models in terms of Correct Classification Rate (CCR). The best model, obtained after variable selection using PLS Discriminant Analysis, yielded CCR values of 82.35 % for EVOO and 66.67 % for VOO in external validation. These results confirmed that VIS + NIRS technology may be used to provide rapid, non-destructive preliminary screening of olive oil samples for categorization; suspect samples may then be analysed by official analytical methods.

Effectiveness of handheld near infrared spectrometer for traceability of Angus steaks

The feasibility of Near Infrared Spectroscopy was assessed for aging traceability of steaks of Angus beef (Biceps femoris) individually vacuum-packaged, as well as for the prediction of the refrigeration storage time (0, 7, and 14 days). For this purpose, a total of 288 steaks homogeneously distributed among the sampling times were used. The model developed by Partial Least Squares-Discriminant Analysis offered high discrimination ability between aged beef vs. non-aged. The accuracy after external validation exceeded 90%. Regarding the predictive capacity of the storage time, it was greater on the set of aged samples, in which the accuracy achieved values higher than 96%, while the accuracy decreased to 75% for the non-aged samples. Results obtained support the ability of NIRS technology to be considered in any digital transformation strategy for traceability across the meat supply chain.

NIR technology for non-destructive monitoring of apple quality during storage

NIR technology for non-destructive monitoring of apple quality during storage

Non-invasive glucose prediction and classification using NIR technology with machine learning

In this paper, a dual wavelength short near-infrared system is described for the detection of glucose levels. The system aims to improve the accuracy of blood glucose detection in a cost-effective and non-invasive way. The accuracy of the method is evaluated using real-time samples collected with the reference finger prick glucose device. A feed forward neural network (FFNN) regression method is employed to predict glucose levels based on the input data obtained from NIR technology. The system calculates glucose evaluation metrics and performs Surveillance error grid (SEG) analysis. The coefficient of determination R2 and mean absolute error are observed 0.99 and 2.49 mg/dl, respectively. Additionally, the system determines the root mean square error (RMSE) as 3.02 mg/dl. It also shows that the mean absolute percentage error (MAPE) is 1.94% and mean squared error (MSE) is 9.16 (mg/dl)2 for FFNN. The SEG analysis shows that the glucose values measured by the system fall within the clinically acceptable range when compared to the reference method. Finally, the system uses the multi-class classification method of the multilayer perceptron (MLP) and K-nearest neighbors (KNN) classifier to classify glucose levels with an accuracy of 99%.

A compact Fourier-transform near-infrared spectrophotometer and chemometrics for characterizing a comprehensive set of seized ecstasy samples

A comprehensive data set of ecstasy samples containing MDMA (N-methyl-3,4-methylenedioxyamphetamine) and MDA (3,4-methylenedioxyamphetamine) seized by the Brazilian Federal Police was characterized using spectral data obtained by a compact, low-cost, near-infrared Fourier-transform based spectrophotometer. Qualitative and quantitative characterization was accomplished using soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA) classification, discriminating partial least square (PLS-DA), and regression models based on partial least square (PLS). By applying chemometric analysis, a protocol can be proposed for the in-field screening of seized ecstasy samples. The validation led to an efficiency superior to 96 % for ecstasy classification and estimating total actives, MDMA, and MDA content in the samples with a root mean square error of validation of 4.4, 4.2, and 2.7 % (m/m), respectively. The feasibility and drawbacks of the NIR technology applied to ecstasy characterization and the compromise between false positives and false negatives rate achieved by the classification models are discussed and a new approach to improve the classification robustness was proposed considering the forensic context.

NIRS technology used for traceability of Cedrela odorata L. commercial shipment in Brazil

The traceability of commercial timber is one of the preventive measures to combat illegal timber trade. Even though there are several methods to determine wood species or its origin, there is no standard procedure established yet. Furthermore, traceability is a difficult problem, and there is a lack of studies about the subject. NIRS technology combined with multivariate data analysis has proved to be a fast and efficient methodology for species identification, as well as a promising tool for geographical origin determination. This study aimed to track a commercial cargo of 19,114 m3 of Cedrela odorata L. boards from the legal extraction concession area in the Jamari National Forest (Amazon region) to the final dealer located in the city of Ubatuba (São Paulo), a distance of 3100 km by land. The cargo was packed and transported by truck to protect it from weather. The NIR spectra obtained at the origin were used to create a data driven soft independent modelling by class analogy(DD-SIMCA model), which was applied to the spectra obtained at the destination of the cargo. The compatibility rate found by the model using all sampled batches was 76 %, which means that most of the samples were within the model acceptance area and were highly compatible with the initial cargo. When applied to C. odorata samples from other origins/cargos, the number of samples within the acceptance area did not go over 21 %. When testing the model on other wood species (Erisma uncinatum Warm. Swietenia macrophylla King, and MicropholismelinonianaPierre), this rate was no more than 22 %. These latter values corroborate the practicality, precision, and reliability of NIRS for in-field tracking of wood cargo.

Incorporating Near Infrared Light Vein Visualization Technology Into Peripheral Intravenous Access Protocols.

Placement of peripheral intravenous catheters (PIVCs) is a frequent occurrence. Yet, PIVCs consistently require multiple attempts for successful cannulation, leading to an increased use of resources and risk of complications. Even though hospitals have established vascular access teams to improve outcomes and increase longevity of PIVCs, not every facility has one, and some struggle to meet demand. In these cases, PIVC placement depends on the confidence and skills of bedside nurses. Difficult access risk identification tools, as well as vein visualization technologies, like near infrared (nIR), have been developed to assist nurses with cannulation. This study sought to explore how hospitals are using vein visualization technology in nurse-driven protocols and to evaluate whether the technology is being meaningfully integrated into venous assessment and PIVC access protocols. In a survey sent to facilities utilizing nIR technology, 48% of respondents incorporated nIR in nurse-driven protocols. Of these respondents, 88% reported improvement in patient satisfaction, 92% saw a reduction in escalations, and 79% reported a reduction in hospital-acquired infections associated with PIVC placement. Integrating vein visualization technology into nurse-driven PIVC placement protocols has the potential to make a positive impact but requires future research to reproduce these findings in clinical studies.

Performance of the caries diagnosis feature of intraoral scanners and near-infrared imaging technology-A narrative review.

To describe and discuss the benefits and drawbacks of various dental caries diagnostic techniques, including the use of intraoral scanners for caries diagnosis based on near-infrared imaging (NIR) technology. A MEDLINE search from 1980-2023 focused on dental caries diagnostic techniques, emphasizing intraoral scanners using NIR technology. Alternative caries detection methods were also evaluated for their advantages and limitations, enabling a comparison with NIR. The review included traditional caries tools, the latest detection methods, and NIR's role in intraoral scanners, drawing from case reports and both in vivo and in vitro studies. Keywords like "caries detection," "intraoral scanners," and "Near Infrared Imaging (NIRI)" guided the search. After screening titles and abstracts for relevance, full texts with valuable insights were thoroughly analyzed. The data was grouped into three: traditional diagnostics, advanced digital methods, and intraoral scanner-based detection. This comprehensive narrative review described and discussed the current state of dental caries diagnostic methods, given the insufficient number of clinical investigations suitable for a systematic review. Traditional caries diagnosis techniques have shown variable accuracy dependent on a dentist's experience and the potential over-removal of healthy tooth structures. Intraoral scanners have emerged as a novel caries detection method, because of their integration of NIR technology. Various studies have confirmed the efficacy of NIR in detecting interproximal caries and in the early diagnosis of non-cavitated caries. Specifically, intraoral scanners have demonstrated promising results, proving comparable to established diagnostic methods like bitewing radiography. Nevertheless, while the integration of NIR into intraoral scanners seems promising, the technology still faces challenges, notably its accuracy in detecting secondary and subgingival cavities. However, with anticipated integrations of AI, NIR in intraoral scanners could revolutionize early caries detection. Intraoral scanners with NIR technology offer non-destructive imaging, real-time lesion visuals, and enhanced patient communication. Although comparable to bitewing radiography in some studies, a universally accepted diagnostic tool is lacking. Future research should compare them with existing methods, focusing on clinical outcomes, cost-effectiveness, and patient acceptance.

Chemical analysis and NIR spectroscopy in the determination of the origin, variety and roast time of Mexican coffee

Coffee is a product whose quality and price are associated with its geographical, genetic and processing origin; therefore, the development of analytical techniques to authenticate the above mentioned is important to avoid adulteration. The objective of this study was to compare conventional analytical methods with NIR technology for the authentication of roasted and ground coffee samples from different producing regions in Mexico (origins) and different varieties. A second objective was to determine, under the same processing conditions, if roasting times can be differentiated by using this technology. A total of 120 samples of roasted and ground commercial coffee were obtained from the states of Chiapas, Oaxaca, Tabasco and Veracruz in Mexico, 30 locally available samples per state. Samples from Veracruz included three different varieties, grown on the same farm and processed under the same conditions. One of these varieties was selected to evaluate the chemical composition of samples roasted at 185 °C using four different roasting times (15, 17, 19 and 21 min). Samples from different producing regions showed significant differences (P < 0.05) in fat content (from 7.45 ± 0.42% in Tabasco to 18.40 ± 2.95% in Chiapas), which was associated with the altitude of coffee plantations (Pearson's r = 0.96). The results indicate that NIR technology generates sufficient useful information to authenticate roasted and ground coffee from different geographical origins in Mexico and different varieties from the same coffee plantation, with similar results to those obtained by conventional analytical methods.

1,2-propanediol in maize silages with nutrients measurement using nirs technology

1,2-propanediol in maize silages with nutrients measurement using nirs technology

IoT System for Gluten Prediction in Flour Samples Using NIRS Technology, Deep and Machine Learning Techniques

Gluten is a natural complex protein present in a variety of cereal grains, including species of wheat, barley, rye, triticale, and oat cultivars. When someone suffering from celiac disease ingests it, the immune system starts attacking its own tissues. Prevalence studies suggest that approximately 1% of the population may have gluten-related disorders during their lifetime, thus, the scientific community has tried to study different methods to detect this protein. There are multiple commercial quantitative methods for gluten detection, such as enzyme-linked immunosorbent assays (ELISAs), polymerase chain reactions, and advanced proteomic methods. ELISA-based methods are the most widely used; but despite being reliable, they also have certain constraints, such as the long periods they take to detect the protein. This study focuses on developing a novel, rapid, and budget-friendly IoT system using Near-infrared spectroscopy technology, Deep and Machine Learning algorithms to predict the presence or absence of gluten in flour samples. 12,053 samples were collected from 3 different types of flour (rye, corn, and oats) using an IoT prototype portable solution composed of a Raspberry Pi 4 and the DLPNIRNANOEVM infrared sensor. The proposed solution can collect, store, and predict new samples and is connected by using a real-time serverless architecture designed in the Amazon Web services. The results showed that the XGBoost classifier reached an Accuracy of 94.52% and an F2-score of 92.87%, whereas the Deep Neural network had an Accuracy of 91.77% and an F2-score of 96.06%. The findings also showed that it is possible to achieve high-performance results by only using the 1452–1583 nm wavelength range. The IoT prototype portable solution presented in this study not only provides a valuable contribution to the state of the art in the use of the NIRS + Artificial Intelligence in the food industry, but it also represents a first step towards the development of technologies that can improve the quality of life of people with food intolerances.

Prediction of Mineral Composition in Wheat Flours Fortified with Lentil Flour Using NIR Technology.

Lentil flour is an important source of minerals, including iron, so its use in food fortification programs is becoming increasingly important. In this study, the potential of near infrared technology to discriminate the presence of lentil flour in fortified wheat flours and the quantification of their mineral composition is evaluated. Three varieties of lentils (Castellana, Pardina and Guareña) were used to produce flours, and a total of 153 samples of wheat flours fortified with them have been analyzed. The results show that it is possible to discriminate fortified flours with 100% efficiency according to their lentil flour content and to discriminate them according to the variety of lentil flour used. Regarding their mineral composition, the models developed have shown that it is possible to predict the Ca, Mg, Fe, K and P content in fortified flours using near infrared spectroscopy. Moreover, these models can be applied to unknown samples with results comparable to ICP-MS determination of these minerals.

High spatial and temporal resolution cerebrovascular reactivity for humans and large mammals: A technological description of integrated fNIRS and niABP mapping system.

Introduction: The process of cerebral vessels maintaining cerebral blood flow (CBF) fairly constant over a wide range of arterial blood pressure is referred to as cerebral autoregulation (CA). Cerebrovascular reactivity is the mechanism behind this process, which maintains CBF through constriction and dilation of cerebral vessels. Traditionally CA has been assessed statistically, limited by large, immobile, and costly neuroimaging platforms. However, with recent technology advancement, dynamic autoregulation assessment is able to provide more detailed information on the evolution of CA over long periods of time with continuous assessment. Yet, to date, such continuous assessments have been hampered by low temporal and spatial resolution systems, that are typically reliant on invasive point estimations of pulsatile CBF or cerebral blood volume using commercially available technology. Methods: Using a combination of multi-channel functional near-infrared spectroscopy and non-invasive arterial blood pressure devices, we were able to create a system that visualizes CA metrics by converting them to heat maps drawn on a template of human brain. Results: The custom Python heat map module works in "offline" mode to visually portray the CA index per channel with the use of colourmap. The module was tested on two different mapping grids, 8 channel and 24 channel, using data from two separate recordings and the Python heat map module was able read the CA indices file and represent the data visually at a preselected rate of 10 s. Conclusion: The generation of the heat maps are entirely non-invasive, with high temporal and spatial resolution by leveraging the recent advances in NIRS technology along with niABP. The CA mapping system is in its initial stage and development plans are ready to transform it from "offline" to real-time heat map generation.

Estimation of the Differential Pathlength Factor for Human Skin Using Monte Carlo Simulations.

Near-infrared technology is an emerging non-invasive technique utilized for various medical applications. Recently, there have been many attempts to utilize NIR technology for the continues monitoring of blood glucose levels through the skin. Different approaches and designs have been proposed for non-invasive blood glucose measurements. Light photons penetrating the skin can undergo multiple scattering events, and the actual optical pathlength becomes larger than the source-to-detector separation (optode spacing) in the reflection-mode configuration. Thus, the differential pathlength factor (DPF) must be incorporated into the modified Beer-Lambert law. The accurate estimation of the DPF values will lead to an accurate quantification of the physiological variations within the tissue. In this work, the aim was to systematically estimate the DPF for human skin for a range of source-to-detector separations and wavelengths. The Monte Carlo (MC) method was utilized to mimic the different layers of human skin with different optical properties and blood and water volume fractions. This work could help improve the accuracy of the near-infrared technique in the measurement of physiological variations within skin tissue.

Uso da espectroscopia no infravermelho próximo para previsão da composição químico-bromatológica de palma forrageira

Four varieties of forage palm (Gigant, Hand of Maiden or Ipa sertânea, Sweet, Little and Ear of elephant mexican) collected in three states of Northeastern Brazil, were read in natura and pre-dried and ground in a NIR Perten DA 7250 instrument. Models for in natura samples showed satisfactory performance for dry matter and crude protein. Better model deviation performance was given for dry matter for samples in natura, dry matter and crude protein, insoluble fiber in acid detergent and in vitro digestibility of dry matter for samples pre-processed. NIR technology can be used to predict the bromatological chemical composition of forage palm. The pre-processing of the sample proved to be efficient, increasing the robustness of the models for the evaluated nutrients, with the exception of crude protein and dry matter, whose performances were considered as models of excellence, even for the in natura sample.

Application of chemometric combination NIRS technology for analysis of mixed coconut oil levels in Aceh Patchouli Oil

Patchouli oil could be a sort of fundamental oil that’s delivered from the refining of the leaves, twigs, and stems of the patchouli plant. One of the problems that occur in determining the quality of patchouli oil is mixing with other oils, such as coconut oil, causing the quality of patchouli oil to be low and the selling price to be cheap. The reason of this inquire about is to construct a show for evaluating the levels of coconut oil mixed in Aceh patchouli oil using the chemometric method. This research uses patchouli oil (crude oil) and coconut oil. The NIRS tool used for spectrum acquisition is the FT-IR IPTEK T-1516. NIRS technology is combined with chemometric methods with two multivariate analyses, namely the PCR and PLS models. The comes about appeared that the PLS calibration show was superior than the PCR calibration model. The RPD value in the non pretreatment PLS calibration model was 3.96 and increased with the presence of SNV pretreatment to 4.78 so that it was included in the category of very good prediction model accuracy. While the PCR calibration model included in the prediction category is still rough, both non-pretreatment and with SNV pretreatment, which are 1.45 and 1.50, respectively.

Efficient monitoring for the nutrient changes in stir-fried Moutan Cortex using non-destructive near-infrared spectroscopy sensors

Now, almost all solutions are moving toward miniaturization of devices. Stir-frying, as a food processing process, presents field analysis challenges to the NIR foundation. Therefore, in this paper, Moutan Cortex, a functional food product that requires strict control of stir-frying degree, with a variety of biological activities such as antioxidant, anti-inflammatory and protection of cardiovascular system, was investigated to realize the qualitative and quantitative analysis of the product on site through miniaturized spectrometer NIR-S-G1. TANGO FT-NIR, a precision benchtop instrument, was applied as a reference to observe the field analysis performance of the instrument. The results show that the handheld device can accurately judge the products of different stir-frying degrees by SVM model with the accuracy rate of 100%. At the same time, it can accurately and quantitatively monitor the four main components in the process. Also combinations of optimization algorithms (Competitive Adaptive Weighted sampling, variable combination population analysis combined with iteratively retaining informative variables and genetic algorithm) and regression models (Partial least squares regression and Support Vector Machine) were investigated to perform a better result. Therefore, this work provided a reference for the field analysis of NIR technology from qualitative and quantitative analysis in this specific analytical application.

Development of non-destructive NIRS models to predict oil and major fatty acid contents of Ethiopian sesame

Sesame is a crucial oilseed crop that contains vital fatty acids. The objective of this study was to build calibration equations using near-infrared reflectance spectroscopy for quality screening of sesame. A total of 136 sesame samples were scanned in the reflectance mode and their wet chemistry was determined by n-hexane extraction and gas chromatography mass spectroscopy. Models for oil and four fatty acids were developed with 110 samples and had an acceptable value of calibration coefficient of determination (R2c), with suitable one minus the ratio of unexplained variance divided by variance (1-VR) value were found except palmitic acid. The prediction of an external validation with 26 datasets revealed a blameless correlation between reference values and NIRS values based on the coefficient of determination of validation (R2v) and relative prediction deviation (RPDv). The models for oil, stearic, oleic, and linoleic acids had suitable values of coefficient of determination of validation and relative prediction deviation, which were more than 2.0 and 0.8, respectively. As a result of this research, it was discovered that the NIRS technology could be used to examine the oil and fatty acid contents of sesame seed qualities directly in breeding program, standard agency and commodity exchanges.

Rapid Superficial Vein Assessment (RaSuVA): A pre-procedural systematic evaluation of superficial veins to optimize venous catheterization in neonates.

Placement of peripheral intra-venous cannulas and epicutaneo-caval catheters is routinely performed in in Neonatal Intensive Care Unit (NICU), and both devices require visible superficial veins easy to cannulate. NICU patients are intrinsically characterized by poor and fragile vein asset, so that puncture and cannulation of superficial veins is often a challenge even for trained clinicians and cannulation frequently results in a stressful, painful, difficult procedure. Rapid Superficial Vein Assessment is meant to offer a systematic pre-procedural evaluation of all superficial veins of the newborn, so to allow a rational choice of the best insertion site, tailored on the single patient, and optimized for the specific type of venous access device. The superficial veins are examined systematically, both with and without NIR technology, exploring seven skin areas in the following order: (1) medial malleolus, (2) lateral malleolus, (3) retro-popliteal fossa, (4) back of the hand and wrist, (5) antecubital fossa, (6) anterior scalp surface, and (7) posterior scalp surface. The aim of the protocol is to increase the first attempt success rate and reduce the duration of the procedure, the number of attempts for single patient and possibly to limit complications, stress, and pain in neonates.