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A trending problem of Extra Virgin Olive Oil (EVOO) adulteration is investigated using two analytical platforms, involving: (1) Near Infrared (NIR) spectroscopy, resulting in a two-way data set, and (2) Fluorescence Excitation-Emission Matrix (EEFM) spectroscopy, producing three-way data. The related instruments were employed to study genuine and adulterated samples. Each data set was first separately analyzed using the Data Driven-Soft Independent Modeling of Class Analogies (DD-SIMCA) method, based on Principal Component Analysis (for the two-way NIR data) and PARallel FACtor analysis (for the three-way EEFM data). The data sets were then processed together using the multi-block fusion method, based on the concept of Cumulative Analytical Signal (CAS). A comparison of the data processing methods in terms of sensitivity, specificity and selectivity showed the following order of excellence: NIR < EEFM < NIR + EEFM. This finding confirms the effectiveness of multi-block data fusion, which cumulatively improves the model performance.

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The glucose level in the blood is measured through invasive methods, causing discomfort in the patient, loss of sensitivity in the area where the sample is obtained, and healing problems. This article deals with the design, implementation, and evaluation of a device with an ESP-WROOM-32D microcontroller with the application of near-infrared photospectroscopy technology that uses a diode array that transmits between 830 nm and 940 nm to measure glucose levels in the blood. In addition, the system provides a webpage for the monitoring and control of diabetes mellitus for each patient; the webpage is hosted on a local Linux server with a MySQL database. The tests are conducted on 120 people with an age range of 35 to 85 years; each person undergoes two sample collections with the traditional method and two with the non-invasive method. The developed device complies with the ranges established by the American Diabetes Association: presenting a measurement error margin of close to 3% in relation to traditional blood glucose measurement devices. The purpose of the study is to design and evaluate a device that uses non-invasive technology to measure blood glucose levels. This involves constructing a non-invasive glucometer prototype that is then evaluated in a group of participants with diabetes.

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The significance of accurate determination of ethanol content in hydrogel formulations was accentuated during COVID-19 pandemic coinciding with the heightened demand for sanitizing agents. The present article proposes three robust methodologies for this purpose: Fourier Transform Infrared Spectroscopy (FTIR), Raman spectroscopy, and Densitometry with matrix effect correction by Near-Infrared Spectroscopy (NIR). All three methods demonstrated outstanding linearity (R2 ≥ 0.99) and minimal errors (< 1.7%), offering simplicity and operational efficiency. FTIR and Raman, being non-destructive and requiring minimal preparation, enable practical on-site analysis capabilities, underscoring the potential of the spectroscopic methods to expedite health investigations and inspections, empowering on-site ethanol determination, and relieving the burden on official laboratories. Additionally, the densitometry with NIR-based approach showcased superior accuracy and precision compared to spectroscopic methods, meeting validation criteria while offering operational advantages over the costly official distillation-based method. Therefore, it stands as a reliable and reproducible technique for comprehensive health and criminal compliance assessments, making it a compelling alternative for both industry and official laboratories.

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The increasing concern over microplastics (MPs) contamination in agricultural soils due to excessive plastic use is a worldwide concern. The objective of this study was to determine which analytical technique is most effective for the analysis of MPs in agricultural soils. Near-infrared spectroscopy (NIR), scanning electron microscopy (SEM), multispectral analysis, and X-ray diffraction were used to analyze sections of clay soil containing varying percentages of virgin white MPs from 0 to 100%. X-ray analysis only detected MPs at high concentrations (20%). However, NIR at 2.300 nm and multispectral analysis at 395 nm demonstrated greater accuracy and sensitivity in distinguishing between all MPs levels. SEM revealed that MPs have an amorphous structure that is distinct from crystalline soil, potentially influencing their interactions with other soil constituents. These findings highlight the value of NIR and multispectral analysis in accurately identifying and measuring MPs in soil. Efficient management plans rely on increased awareness of MPs' environmental impact.

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Maize (Zea mays L.) is of socioeconomic importance as an essential food for human and animal nutrition. However, cereals are susceptible to attack by mycotoxin-producing fungi, which can damage health. The methods most commonly used to detect and quantify mycotoxins are expensive and time-consuming. Therefore, alternative non-destructive methods are required urgently. The present study aimed to use near-infrared spectroscopy with hyperspectral imaging (NIR-HSI) and multivariate image analysis to develop a rapid and accurate method for quantifying fumonisins in whole grains of six naturally contaminated maize cultivars. Fifty-eight samples, each containing 40 grains, were subjected to NIR-HSI. These were subsequently divided into calibration (38 samples) and prediction sets (20 samples) based on the multispectral data obtained. The averaged spectra were subjected to various pre-processing techniques (standard normal variate (SNV), first derivative, or second derivative). The most effective pre-treatment performed on the spectra was SNV. Partial least squares (PLS) models were developed to quantify the fumonisin content. The final model presented a correlation coefficient (R2) of 0.98 and root mean square error of calibration (RMSEC) of 508 µg.kg-1 for the calibration set, an R2 of 0.95 and root mean square error of prediction (RMSEP) of 508 µg.kg-1 for the test validation set and a ratio of performance to deviation of 4.7. It was concluded that NIR-HSI with partial least square regression is a rapid, effective, and non-destructive method to determine the fumonisin content in whole maize grains.

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Artisanal cheeses are part of the heritage and identity of different countries or regions. In this work, we investigated the spectral variability of a wide range of traditional Brazilian cheeses and compared the performance of different spectrometers to discriminate cheese types and predict compositional parameters. Spectra in the visible (vis) and near infrared (NIR) region were collected, using imaging (vis/NIR-HSI and NIR-HSI) and conventional (NIRS) spectrometers, and it was determined the chemical composition of seven types of cheeses produced in Brazil. Principal component analysis (PCA) showed that spectral variability in the vis/NIR spectrum is related to differences in color (yellowness index) and fat content, while in NIR there is a greater influence of productive steps and fat content. Partial least squares discriminant analysis (PLSDA) models based on spectral information showed greater accuracy than the model based on chemical composition to discriminate types of traditional Brazilian cheeses. Partial least squares (PLS) regression models based on vis/NIR-HSI, NIRS, NIR-HSI data and HSI spectroscopic data fusion (vis/NIR + NIR) demonstrated excellent performance to predict moisture content (RPD > 2.5), good ability to predict fat content (2.0 < RPD < 2.5) and can be used to discriminate between high and low protein values (∼1.5 < RPD < 2.0). The results obtained for imaging and conventional equipment are comparable and sufficiently accurate, so that both can be adapted to predict the chemical composition of the Brazilian traditional cheeses used in this study according to the needs of the industry.

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The aims of this study were to predict carcass and meat traits, as well as the chemical composition of the 9th to 11th rib sections of beef cattle from portable NIR spectra. The 9th to 11th rib section was obtained from 60 Nellore bulls and cull cows. NIR spectra were acquired at: P1 -center of Longissimus muscle; and P2 -subcutaneous fat cap. The models accurately estimated (P ≥ 0.083) all carcass and meat quality traits, except those for predicting red (a*) and yellow (b*) intensity from P1, and 12th-rib fat from P2. However, precision was highly variable among the models; those for the prediction of carcass pHu, 12th rib fat, toughness from P1, and those for 12th rib fat, a* and b* from P2 presented high precision (R2 ≥ 0.65 or CCC ≥ 0.63), whereas all other models evaluated presented moderate to low precision (R2 ≤ 0.39). Models built from P1 and P2 accurately estimated (P ≥ 0.066) the chemical composition of the meat plus fat, bones and, meat plus fat plus bones, except those for predicting the ether extract (EE) and crude protein (CP) of bones and the EE of Meat plus bones fraction from P2. However, precision was highly variable among the models (-0.08 ≤ R2 ≤ 0.86) of the 9th and 11th rib section. Those models for the prediction of dry matter (DM) and EE of the bones from P1; of EE from P1; and of EE, mineral matter (MM), CP from P2 of meat plus fat plus bones presented high precision (R2 ≥ 0.76 or CCC ≥ 0.62), whereas all other models evaluated presented moderate to low precision (R2 ≤ 0.45). Thus, models built from portable NIR spectra acquired at different points of the 9th to 11th rib section were recommended for predicting carcass and muscle quality traits as well as for predicting the chemical composition of this section of beef cattle. However, it is noteworthy, that the small sample size was one of the limitations of this study.

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OBJECTIVE: This study aimed to delineate the recovery patterns of regional oxygen saturation (SrO2) in pediatric cardiac surgery patients subjected to remote ischemic preconditioning (RIPC), utilizing near-infrared spectroscopy (NIRS) for quantification. It also sought to establish the correlation between these perfusion patterns and postoperative clinical outcomes. DESIGN: A prospective longitudinal observational study. SETTING: The study was conducted at Fundación Valle Del Lili, a high-complexity service provider institution in Fundación Valle Del Lili. PARTICIPANTS: Pediatric patients (younger than 18 years of age) scheduled for elective cardiac surgery requiring cardiopulmonary bypass between August 2022 and July 2023. INTERVENTIONS: RIPC was performed after anesthetic induction, involving cycles of ischemia and reperfusion on a lower limb. Monitoring included SrO2 using NIRS. MEASUREMENTS AND MAIN RESULTS: The study identified 4 distinct patterns of SrO2 during RIPC. Findings demonstrated a significant association between the negative SrO2 pattern and increased postoperative adverse events, including extended hospital stays and higher mortality, while a positive pattern was associated with better outcomes. CONCLUSIONS: Specific patterns of SrO2 response to RIPC may serve as important indicators for risk stratification in congenital heart surgery. This study illustrated the potential of NIRS in detecting hypoxic states and predicting postoperative outcomes, emphasizing the need for standardized clinical interpretation of RIPC patterns.

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Fingerprinting is one of the most commonly used techniques to obtain pieces of evidence for identification of individuals. An estimation of how long a trace has been left at a crime scene could represent an important improvement for criminal investigations. There is no reliable analytical method, however, to estimate the age of a fingerprint, since this is an uncontrolled process and changes are affected by factors such as environmental conditions. This study aims to better understand the aging process of fingerprints and identify the relevant variables and limitations of the fingerprint aging process using near infrared hyperspectral imaging (NIR-HSI). For this purpose, aging of the fingerprints of 13 volunteers was evaluated using partial least squares - discriminant analysis (PLS-DA) as a preliminary exploratory approach. Four different modelling approaches were evaluated. The percentage of correctly classified pixels varied from 20.92% to 66.67%. An analysis of the associated spectra found that during the first days of aging the degradation of fat-soluble components, as well as the elimination/absorption of water, seemed to follow non-uniform trends and vary in degradation rate from donor to donor. Better classification tended to occur over longer aging times.

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Digestibility and intake are parameters difficult and expensive to estimate under grazing conditions; therefore, the aim of this study was to develop near-infrared reflectance spectroscopy (NIRS) calibrations applied to feces (F-NIRS) and evaluate their accuracy to predict dry matter digestibility (DMD) and dry matter intake (DMI) of Colombian creole cattle. Five digestibility trials using creole steers were conducted; indigestible neutral detergent fiber (iNDF) was used as internal marker and Cr2O3 and TiO2 as external markers. A total of 249 forage and 396 fecal samples from individual animals were collected, dried, and grinded for conventional chemical analysis. For spectral analysis, fecal samples were pooled across collection periods (77 samples). Chemometric analysis was performed using WinISI V4.10 software applying the modified partial least squares method. Cross-validation was performed to avoid overfitting the models. The goodness-of-fit statistics considered were the coefficient of determination in cross-validation and prediction sets (R2cv and r2, respectively) and the ratio performance deviation (RPD). Fecal NIRS calibrations developed for forage and supplement DMD showed a satisfactory fit (R2cv =0.87 and RPD=2.77 and R2cv=0.92 and RPD=3.50, respectively). The accuracy of fecal output equations using chromium (Cr) and titanium (Ti) was similar in terms of R2cv (0.92) and RPD (3.63 vs. 3.57). Total DMI equations using Ti performed better compared to Cr (R2cv = 0.82 vs. 0.78; RPD=2.41 vs. 2.17, respectively). The F-NIRS models were validated using a completely independent set of fecal samples showing a moderate fit (r2>0.8 and RPD>2.0). This study showed that F-NIRS is a feasible tool to predict DMD and DMI of creole steers under grazing conditions. However, previous to socialization, this requires an improvement in accuracy of the calibrated equations related to grazing animals in different production contexts.

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Robusta Amazônico is the name given to the Amazonian coffee that has been becoming popular and has recently been registered as a geographical indication in Brazil. It is produced by indigenous and non-indigenous coffee producers in regions that are geographically very close to one another. There is a need to authenticate whether coffee is truly produced by indigenous people and near-infrared (NIR) spectroscopy is an excellent technique for this. To meet the substantial trend towards NIR spectroscopy miniaturization, this work compared benchtop and portable NIR instruments to discriminate Robusta Amazônico samples using partial least squares discriminant analysis (PLS-DA). To ensure the results to be fairly comparable and, at the same time, to guarantee representative selection of both training and test set for the discriminant analysis, a sample selection strategy based on coupling ComDim multi-block analysis and the duplex algorithm was applied. Different pre-processing techniques were tested to create multiple matrices to be used in ComDim, as well as to build the discriminant models. The best PLS-DA model for benchtop NIR provided an accuracy of 96% for the test samples, while for the portable NIR the correct classification rate was 92%. It was demonstrated that portable NIR provides similar results to benchtop NIR for coffee origin classification by performing an unbiased sample selection strategy.

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Introducción: La litiasis urinaria es una enfermedad común, cuya prevalencia se incrementa a escala nacional y planetaria. Objetivos: Conocer la composición de las urolitiasis en pacientes adultos cubanos y su relación con los trastornos metabólicos renales. Métodos: Estudio descriptivo, transversal. Universo constituido por los pacientes cubanos de 19 años y más de edad, que se realizaron estudio de composición de urolitiasis en el Instituto de Nefrología Dr. Abelardo Buch, de La Habana, Cuba, en el período comprendido de 2011-2020. De ellos 443 se habían realizado estudio metabólico renal. Los datos fueron recogidos de los informes de resultados, de composición de litiasis y de estudio metabólico. Se utilizó análisis de distribución de frecuencias, y para identificar las relaciones, el test independencia. Resultados: En cuanto a la composición química, predominaron las litiasis de oxalato de calcio. Los trastornos metabólicos más frecuentes fueron excreción de sodio aumentada (46,7 por ciento) y volumen urinario bajo (29,3 por ciento). La frecuencia de pacientes con litiasis cálcicas, fue superior en los que tuvieron excreción de sodio aumentada (78,3 por ciento), y en los que presentaron hipercalciuria (83,3 por ciento), en contraste con las frecuencias de este tipo de litiasis, en los que no presentaron dichos trastornos (p=0,03 en ambos casos). Conclusiones: Las urolitiasis más comunes en adultos cubanos son las cálcicas, especialmente las de oxalato de calcio. Los trastornos metabólicos más frecuentes son: excreción urinaria aumentada de sodio, volumen urinario bajo y pH urinario ácido. La presencia de litiasis cálcicas se relaciona con excreción urinaria aumentada de sodio y con hipercalciuria(AU)

Introduction: Urinary lithiasis is a common disease, whose prevalence is increasing on a national and planetary scale. Objectives: To know the composition of urolithiasis in Cuban adult patients and its relationship with renal metabolic disorders. Methods: Descriptive, cross-sectional study. Universe constituted by Cuban patients aged 19 and over, who underwent a composition study of urolithiasis at the Dr. Abelardo Buch Institute of Nephrology, in Havana, Cuba, in the period 2011-2020. In 443 of them, a renal metabolic study had also been carried out. The data were collected from the results reports of stone composition and metabolic study. Frequency distribution analysis was used, and the independence test was used to identify relationships. Results: Regarding chemical composition, calcium oxalate stones predominated. The most frequent metabolic disorders were increased sodium excretion (46.7percent) and low urine volume (29.3percent). The frequency of patients with calcium stones was higher in those with increased sodium excretion (78.3percent) and in those with hypercalciuria (83.3percent), in contrast with the frequencies of this type of lithiasis, in those who did not present these disorders (p=0.03 in both cases). Conclusions: The most common urolithiasis in Cuban adults are calcium ones, especially those of calcium oxalate. The most common metabolic disorders are: increased urinary sodium excretion, low urinary volume and acid urinary pH. The presence of calcium lithiasis is related to increased urinary sodium excretion and hypercalciuria(AU)

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Objective.To establish the reference values for peripheral tissue perfusion of the triceps surae muscle assessed by Near-infrared spectroscopy (NIRS) at rest and in progressive effort.Approach.A total of 288 apparently healthy individuals of both sexes were included; between 30 and 79 years of age; nonsmokers; without diagnosis of diabetes mellitus, systemic arterial hypertension, kidney disease, symptoms of angina and intermittent claudication, or any musculoskeletal alteration that would prevent physical exertion; and without diagnosis of Peripheral arterial disease (PAD) or other associated symptoms. All individuals performed anthropometric measurements, physical activity levels, and tissue oxygen saturation (StO2) assessments by NIRS during and after arterial occlusion maneuver and incremental shuttle walking test. The variables obtained by NIRS were presented in percentiles (P) for general comparison between sexes and for comparison between sexes according to age group. The relationship between the NIRS data and other variables was tested.Main results.Considering P50 and p<0.05, men had lower StO2 values, higher deoxygenation and reoxygenation (Tx-reox) rates at rest, and higher Tx-reox during progressive effort. There were correlations (p<0.0001) of body composition with the lowest StO2 and Tx-reox values and of functional capacity with Tx-reox in occlusion and Tx-reox.Significance.The percentiles presented can clinically assist in the diagnosis and monitoring of different health conditions; however, it is important to consider the individual's sex, body composition, and functional capacity.

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Concepts from data science, machine learning, deep learning and artificial neural networks are spreading in many disciplines. The general idea is to exploit the power of statistical tools to interpret complex and, in many cases, non-linear data. Specifically in analytical chemistry, many chemometrics tools are being developed. However, they tend to get more complex without necessarily improving the prediction ability, which conspires against parsimony. In this report, we show how non-linear analytical data sets can be solved with equal or better efficiency by easily interpretable modified linear models, based on the concept of local sample selection before model building. The latter activity is conducted by choosing a sub-set of samples located in the neighborhood of each unknown sample in the space spanned by the latent variables. Two experimental examples related to the use of near infrared spectroscopy for the analysis of target properties in food samples are examined. The comparison with seemingly more complex chemometric models reveals that local regression is able to achieve similar analytical performance, with considerably less computational burden.

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The quality control for fruit maturity inspection is a key issue in fruit packaging and international trade. The quantification of Soluble Solids (SS) in fruits gives a good approximation of the total sugar concentration at the ripe stage, and on the other hand, SS alone or in combination with acidity is highly related to the acceptability of the fruit by consumers. The non-destructive analysis based on Visible (VIS) and Near-Infrared (NIR) spectroscopy has become a popular technique for the assessment of fruit quality. To improve the accuracy of fruit maturity inspection, VIS−NIR spectra models based on machine learning techniques are proposed for the non-destructive evaluation of soluble solids in considering a range of variations associated with varieties of stones fruit species (peach, nectarine, and plum). In this work, we propose a novel approach based on a Convolutional Neural Network (CNN) for the classification of the fruits into species and then a Feedforward Neural Network (FNN) to extract the information of VIS−NIR spectra to estimate the SS content of the fruit associated to several varieties. A classification accuracy of 98.9% was obtained for the CNN classification model and a correlation coefficient of Rc>0.7109 for the SS estimation of the FNN models was obtained. The results reported show the potential of this method for a fast and on-line classification of fruits and estimation of SS concentration.

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A recent case of contamination of some batches of a Brazilian beer brand with diethylene glycol (DEG) had great repercussion, resulting in at least seven deaths. In this article, a direct method was developed for the rapid detection of DEG in beer samples based on portable near-infrared spectroscopy combined with partial least squares discriminant analysis (PLS-DA). The discriminant model was built with 100 uncontaminated beer samples and 100 samples containing DEG in a concentration range between 10 and 1000 mg L-1, totalizing 200 samples of different brands and styles. The method was validated by estimating figures of merit, such as false positive and false negative rates, sensitivity, specificity, accuracy, accordance, and concordance. The decision limit (CCα) of the method was 52 mg L-1 and the detection capability (CCß) was 106 mg L-1. This method does not consume reagents/solvents and can be suitable for the beer industry quality control or forensic investigations.

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The dairy products sector is an important part of the food industry, and their consumption is expected to grow in the next 10 years. Therefore, the authentication of these products in a faster and precise way is required for the sake of public health. This review proposes the use of near-infrared techniques for the detection of food fraud in dairy products as they are faster, nondestructive, environmentally friendly, do not require sample preparation, and allow multiconstituent analysis. First, we have described frequent forms of food fraud in dairy products and the application of traditional techniques for their detection, highlighting gaps and counterproductive characteristics for the actual global food chain, as longer sample preparation time and use of reagents. Then, the application of near-infrared spectroscopy and hyperspectral imaging for the detection of food fraud mainly in cheese, butter, and yogurt are described. As these techniques depend on model development, the coverage of different dairy products by the literature will promote the identification of food fraud in a faster and reliable way.

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La espectroscopia cercana infrarroja (NIRS, por su sigla en inglés), es una técnica óptica no invasiva y no ionizante utilizada para medir la oxigenación tisular regional a través de sensores transcutáneos. En los últimos años, han aumentado de manera exponencial las publicaciones sobre este tema; esto refleja el creciente interés de investigadores y clínicos por la utilización de esta nueva tecnología y los beneficios que podría ofrecerles a los pacientes pediátricos. El objetivo de esta revisión es dar a conocer el funcionamiento y las posibles aplicaciones de la saturación regional medida por NIRS, así como los desafíos en el futuro.

Near infrared spectroscopy (NIRS) is a non-invasive optical technique for the evaluation of regional tissue oxygenation using transcutaneous detectors. In recent years, publications about this topic have increased exponentially; this reflects the growing interest among investigators and clinicians about this new technology and its potential benefits for pediatric patients. The objective of this review is to know the functioning and potential uses of regional saturation measured by NIRS and establish future challenges.

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Several clinical conditions leading to traumatic brain injury can cause hematomas or edemas inside the cerebral tissue. If these are not properly treated in time, they are prone to produce long-term neurological disabilities, or even death. Low-cost, portable and easy-to-handle devices are desired for continuous monitoring of these conditions and Near Infrared Spectroscopy (NIRS) techniques represent an appropriate choice. In this work, we use Time-Resolved (TR) Monte Carlo simulations to present a study of NIR light propagation over a digital MRI phantom. Healthy and injured (hematoma/edema) situations are considered. TR Diffuse Reflectance simulations for different lesion volumes and interoptode distances are performed in the frontal area and the left parietal area. Results show that mean partial pathlengths, photon measurement density functions and time dependent contrasts are sensitive to the presence of lesions, allowing their detection mainly for intermediate optodes separations, which proves that these metrics represent robust means of diagnose and monitoring. Conventional Continuous Wave (CW) contrasts are also presented as a particular case of the time dependent ones, but they result less sensitive to the lesions, and have higher associated uncertainties.

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Near infrared spectroscopy (NIRS) is a non-invasive optical technique for the evaluation of regional tissue oxygenation using transcutaneous detectors. In recent years, publications about this topic have increased exponentially; this reflects the growing interest among investigators and clinicians about this new technology and its potential benefits for pediatric patients. The objective of this review is to know the functioning and potential uses of regional saturation measured by NIRS and establish future challenges.

La espectroscopia cercana infrarroja (NIRS, por su sigla en inglés), es una técnica óptica no invasiva y no ionizante utilizada para medir la oxigenación tisular regional a través de sensores transcutáneos. En los últimos años, han aumentado de manera exponencial las publicaciones sobre este tema; esto refleja el creciente interés de investigadores y clínicos por la utilización de esta nueva tecnología y los beneficios que podría ofrecerles a los pacientes pediátricos. El objetivo de esta revisión es dar a conocer el funcionamiento y las posibles aplicaciones de la saturación regional medida por NIRS, así como los desafíos en el futuro.

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