Near-infrared Spectroscopy Instrument Research Articles

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.

A correction method for mitigating absorbance discrepancies between near-infrared spectrometers through the incorporation of blended carbon-titanium dioxide powder

In near-infrared spectroscopy analysis, ensuring the accurate transfer of models between different instruments relies on maintaining the accuracy of instrument wavelengths and absorbance. To mitigate absorbance drift at different wavelength points, this paper proposes a near-infrared spectroscopy point-by-point quadratic polynomial correction method based on carbon-titanium dioxide powder samples. The method establishes a quadratic polynomial relationship model for the absorbance of each wavelength point between the main instrument and slave instruments. The study utilized two S450 grating-based diffuse reflection near-infrared spectroscopy instruments, with one serving as the main instrument and the other as the slave instrument. The point-by-point quadratic polynomial was employed to correct wheat spectra collected by the slave instruments, and a crude protein content prediction model for wheat was established, comparing it with linear regression correction. After correction, the average Euclidean distance of wheat spectra decreased by 66.71%, from 0.0937 to 0.0321, and the average peak-valley Euclidean distance decreased by 72.28%, from 0.0203 to 0.0056. The standard deviation of the predicted results decreased by 90.69%, from 1.4372 to 0.1338. The correction effect of the method combined with traditional preprocessing methods was superior to using preprocessing methods alone. Overall, the near-infrared spectroscopy point-by-point quadratic polynomial correction method based on carbon-titanium dioxide powder samples significantly reduces spectral differences between different instruments, enhances spectral consistency, and diminishes prediction errors, achieving improved model sharing between instruments.

A Novel Approach to Evaluating Crosstalk for Near-Infrared Spectrometers

Multi-channel and multi-parameter near-infrared spectroscopy (NIRS) has gradually become a new research direction and hot spot due to its ability to provide real-time, continuous, comprehensive indicators of multiple parameters. However, multi-channel and multi-parameter detection may lead to crosstalk between signals. There is still a lack of benchmarks for the evaluation of the reliability, sensitivity, stability and response consistency of the NIRS instruments. In this study, a set of test methods (a human blood model test, ink drop test, multi-channel crosstalk test and multi-parameter crosstalk test) for analyzing crosstalk and verifying the reliability of NIRS was conducted to test experimental verification on a multi-channel (8-channel), multi-parameter (4-parameter) NIRS instrument independently developed by our team. Results show that these tests can be used to analyze the signal crosstalk and verify the reliability, sensitivity, stability and response consistency of the NIRS instrument. This study contributes to the establishment of benchmarks for the NIRS instrument crosstalk and reliability testing. These novel tests have the potential to become the benchmark for NIRS instrument reliability testing.

Spectraformer: deep learning model for grain spectral qualitative analysis based on transformer structure.

This study delves into the use of compact near-infrared spectroscopy instruments for distinguishing between different varieties of barley, chickpeas, and sorghum, addressing a vital need in agriculture for precise crop variety identification. This identification is crucial for optimizing crop performance in diverse environmental conditions and enhancing food security and agricultural productivity. We also explore the potential application of transformer models in near-infrared spectroscopy and conduct an in-depth evaluation of the impact of data preprocessing and machine learning algorithms on variety classification. In our proposed spectraformer multi-classification model, we successfully differentiated 24 barley varieties, 19 chickpea varieties, and ten sorghum varieties, with the highest accuracy rates reaching 85%, 95%, and 86%, respectively. These results demonstrate that small near-infrared spectroscopy instruments are cost-effective and efficient tools with the potential to advance research in various identification methods, but also underscore the value of transformer models in near-infrared spectroscopy classification. Furthermore, we delve into the discussion of the influence of data preprocessing on the performance of deep learning models compared to traditional machine learning models, providing valuable insights for future research in this field.

Nondestructive rapid identification of wild Cordyceps sinensis with portable instrument.

Cordyceps sinensis (CS) is a precious medicinal fungus. Wild CS (WCS) and artificial CS (ACS) are destroyed for their identification using traditional methods, which are time consuming and labor-intensive. Therefore, it is crucial to establish a nondestructive identification method to rapidly screen WCS. The aim of this study was to provide technical support for rapid screening of CS and evaluation of its quality. The applicability of the model was improved through model transfer. In this study, continuous wavelet transform was used to analyze the differences in moisture content and active components between WCS and ACS from the perspective of characteristic molecular groups. A portable instrument and a laboratory benchtop instrument were used to determine CS spectra. Partial least squares discrimination analysis was conducted for the identification of WCS and ACS while preserving the original shape of CS. Moreover, improved principal component analysis was utilized to transfer the model between the two types of near-infrared spectroscopy (NIRS) instruments. The results demonstrated that three peaks, at 1443, 1941, and 2183 nm, were characteristic absorption peaks. The model based on NIRS could initially provide rapid differentiation between WCS and ACS. At the same time, the accuracy of the external test set was further improved to over 95% through forward transfer. Therefore, this method could be used for rapid screening of WCS and provides technical support for the nondestructive identification of CS and initial assessment of CS quality.

Point-of-care detection of fibrosis in liver transplant surgery using near-infrared spectroscopy and machine learning.

Visual assessment and imaging of the donor liver are inaccurate in predicting fibrosis and remain surrogates for histopathology. We demonstrate that 3-s scans using a handheld near-infrared-spectroscopy (NIRS) instrument can identify and quantify fibrosis in fresh human liver samples. We undertook NIRS scans on 107 samples from 27 patients, 88 from 23 patients with liver disease, and 19 from four organ donors. Liver disease patients had a median immature fibrosis of 40% (interquartile range [IQR] 20-60) and mature fibrosis of 30% (10%-50%) on histopathology. The organ donor livers had a median fibrosis (both mature and immature) of 10% (IQR 5%-15%). Using machine learning, this study detected presence of cirrhosis and METAVIR grade of fibrosis with a classification accuracy of 96.3% and 97.2%, precision of 96.3% and 97.0%, recall of 96.3% and 97.2%, specificity of 95.4% and 98.0% and area under receiver operator curve of 0.977 and 0.999, respectively. Using partial-least square regression machine learning, this study predicted the percentage of both immature (R 2 = 0.842) and mature (R 2 = 0.837) with a low margin of error (root mean square of error of 9.76% and 7.96%, respectively). This study demonstrates that a point-of-care NIRS instrument can accurately detect, quantify and classify liver fibrosis using machine learning.

A correction method for mitigating inter-instrumental absorbance drift in grating-based near-infrared spectrometers

In near-infrared spectroscopy analysis, the accuracy of instrument wavelength and breadth is crucial as it forms the foundation for transferring different instrument models. Wavelength and absorbance drift in grating-based scanning instruments result from differences in the grating, detector, and wavelength scanning system. Spectral wavelength drift can be corrected using materials such as polyethylene and polystyrene, which have absorption peaks in the near-infrared region. To reduce absorbance drift in spectra at different wavelength points, this article proposes a point-by-point linear correction method for near-infrared spectra using a small number of typical agricultural product samples. The method constructs a linear relationship model for the absorbance of each wavelength point between the main instrument and slave instruments. The study used seven S450 grating-based diffuse reflection near-infrared spectroscopy instruments, one serving as the main instrument and the remaining six as slave instruments. The point-by-point linear correction method was used to correct wheat spectra collected by the slave instruments, and a crude protein content model for wheat was established for prediction. The results showed that the method reduces spectral differences between different instruments, improves spectral consistency, and reduces prediction errors, achieving better model sharing between instruments. After correcting, the average normalized variation coefficient of wheat spectra decreased by 95.12%, from 7.78% to 0.38%, and the average standard deviation of the predicted results decreased by 78.18%, from 0.5321 to 0.1161. The correction effect of the method combined with traditional pre-processing methods was better than using pre-processing methods alone. Overall, the point-by-point correction method based on a small number of typical agricultural product samples has a significant effect on improving the accuracy of near-infrared spectroscopy analysis.

First step for hand-held NIRS instrument field use: Table grape quality assessment consideration of temperature and sunlight chemometrics correction

Changes of temperature and sunlight disturb the spectra and furtherly influence the prediction outcome of near infrared spectroscopy (NIRS) in assessment of fruit quality during field measurement. The hand-held NIRS device combination of chemometrics method was investigated for correcting the influence of temperature and sunlight to the grape spectra. The distribution of the soluble solids content (SSC) within one bunch was analyzed, and the middle region of the bunch was recommended for NIRS sampling position. Chemometrics algorithms of global model, external parameter orthogonalisation (EPO) and generalised least square weighting (GLSW) were employed to correct the influence of temperature and sunlight. Comparison of global model and GLSW, EPO improved the performance of the partial least square regression (PLSR) models with coefficient of determination (Rp2) of 0.88–0.90, root mean square error of prediction (RMSEPiv) in the internal validation set of 0.89–0.94% and ratio of standard deviation (SD) to RMSEPiv (RPD) of 3.27–3.14 in predicting the samples at three temperatures and Rp2 of 0.98, RMSEPiv of 0.50% and RPD of 7.00 outdoor measurement. The results suggested that it was feasibility for correcting the influence of temperature and sunlight to the hand-held NIRS device to assess the grape bunch quality for outdoor use.

Designing Photoelectric Sensor Module in Infrared Communication and Applying Wireless Sensor Network in Leakage Cable Detection

With technological advancement, Near-infrared (NIR) spectroscopy instruments have gradually entered people’s daily life and production with their unique performance. Therefore, it has a positive significance in researching NIR spectroscopy analysis technology. This work designs a refrigerating Lead Sulfide (PbS) NIR photoelectric sensor P2664-05 and its photoelectric signal conversion circuit. The converted voltage signal is used for subsequent circuit processing through the amplification and frequency selection circuit. The aim is to ensure the sensor’s corresponding detectable spectral wavelength, sensitivity, and signal-to-noise ratio are improved under low temperatures. To this end, the refrigeration current generation circuit is designed. The refrigerating temperature is set below −15 °C, and the refrigerating current is 500 mA. At the same time, it is necessary to monitor whether the sensor’s temperature state meets the system stability requirements during the refrigeration process. The designed sensor module is connected to the instrument system in the test, and the whiteboard without spectral absorption is scanned. The results show that the designed NIR sensor’s shortcomings in noise processing performance have been improved using a System on a Programmable Chip (SoPC) as a processor. An infrared communication node is designed using its built-in digital resources and TFDU infrared communication chip. The designed NIR sensor and communication node are added to the 485 bus to build a communication network. In leakage cable detection, the communication network has higher security, lower error rate, and lower power consumption.

BIAN: A Multilayer Microfluidic-Based Tissue-Mimicking Phantom for Near-Infrared Imaging.

Near-infrared spectroscopy (NIRS) is a non-invasive optical method for monitoring cerebral oxygenation. Changes in regional blood flow and oxygenation due to neurovascular coupling are important biomarkers of neuronal activation. So far, there has been little research on multilayer tissue phantoms with tuneable blood flow, blood volume, and optical properties to simulate local changes in oxygenation at different depths. The aim of this study is to design, fabricate and characterize a complex dynamic phantom based on multilayer microfluidics with controllable blood flow, blood volume, and optical properties for testing NIRS instruments. We developed a phantom prototype with two microfluidic chips embedded at two depths inside a solid silicone phantom to mimic the vessels in the scalp and in the cortex. To simulate the oxygenation and perfusion of tissue, a solution with blood-like optical properties was sent into the microchannels by a pump with a programmable pressure controller. The pressure adjusted the volume of the microfluidic chips representing a distension of blood vessels. The optical changes in the superficial and deep layers were measured by a commercially available frequency domain NIRS instrument. The NIRS successfully detected the changes in light intensity elicited by the changes in the pressure input to the two layers. In conclusion, the microfluidics-based imaging phantom was successfully designed and fabricated and mimics brain functional activity. This technique has great potential for testing other optical devices, e.g., diffuse correlation spectroscopy, pulse oximetry, and optical coherence tomography.

Low-dissipation optimization of the prefrontal cortex in the -12° head-down tilt position: A functional near-infrared spectroscopy study.

Our present study set out to investigate the instant state of the prefrontal cortex (PFC) in healthy subjects before and after placement in the -12°head-down tilt (HDT) position in order to explore the mechanism behind the low-dissipation optimization state of the PFC. 40 young, right-handed healthy subjects (male: female = 20: 20) were enrolled in this study. Three resting state positions, 0°initial position, -12°HDT position, and 0°rest position were sequentially tested, each for 10 minutes. A continuous-wave functional near-infrared spectroscopy (fNIRS) instrument was used to assess the resting state hemodynamic data of the PFC. After preprocessing the hemodynamics data, we evaluated changes in resting-state functional connectivity (rsFC) level and beta values of PFC. The subjective visual analogue scale (VAS) was applied before and after the experiment. The presence of sleep changes or adverse reactions were also recorded. Pairwise comparisons of the concentrations of oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and hemoglobin (HbT) revealed significant differences in the aforementioned positions. Specifically, the average rsFC of PFC showed a gradual increase throughout the whole process. In addition, based on graph theory, the topological properties of brain network, such as small-world network and nodal degree centrality were analyzed. The results show that global efficiency and small-world sigma (σ) value were differences between 0°initial and 0°rest. In this study, placement in the -12°HDT had a significant effect on PFC function, mainly manifested as self-inhibition, decreased concentration of HbO in the PFC, and improved rsFC, which may provide ideas to the understanding and explanation of neurological diseases.

Authenticity of almond flour using handheld near infrared instruments and one class classifiers

Almonds (Prunus dulcis) are highly nutritious fruit seeds and are widely consumed, mainly in powder form (flour). With considerable economic value, this product has become a candidate for adulterations to increase profit by lowering the product cost. In the present work, methods were developed for identification of almond flour adulteration, using near infrared spectroscopy (NIR). Three different portable NIR instruments were evaluated to verify the authenticity of almond flours and the results were compared with a benchtop FT-NIR. Fifty-four almond flours of different varieties were adulterated with low-cost flours widely consumed in Brazil. Soft independent modeling of class analogies (SIMCA), Data driven SIMCA (DD-SIMCA), and One-class partial least squares (OCPLS) were used as classification methods. PLSR was employed to predict the purity of the sample. The classification results achieved 100% sensitivity and more than 95% specificity for samples with adulterant concentration of 5% (w/w) or above. The PLS models showed coefficients of determination (R²) greater than 0.90 for all models and RMSEP values between 3.2% and 4.8% for purity. The results of the multivariate models indicate that the portable NIR instruments are efficient for the identification and quantification of adulterated almond flour.

Adaptive Artificial Neural Network in near infrared spectroscopy for standard-free calibration transfer

Near infrared spectroscopy (NIRS) is a secondary analytical method that could use machine learning to predict components of interest for various applications. Calibration transfer (CT) algorithms have been used to transfer the existing NIRS calibration model to another NIRS instruments to reduce the involvement of expensive and tedious conventional chemical analytical methods. However, most of CT algorithms involve both primary and secondary instruments to acquire NIRS signals from standard samples for spectral standardization. On the other hand, artificial neural network (ANN) that can adapt to new environmental conditions may be an alternative standard-free CT algorithm in transferring a trained ANN to secondary instruments without the involvement of the primary instrument. Although ANN has been widely implemented in NIRS research as a calibration model, its feasibility as an alternative CT method has not been evaluated. Thus, this study evaluates the feasibility of an adaptive ANN (AANN) in transferring ANN model from primary to secondary instruments using two traceable NIRS datasets. First, ANN and ANN with principal components (PCs-ANN) were developed and optimized using Bayesian learning algorithm. After that, these ANNs were adapted to secondary instruments using respective transfer data, in which, only the weights and biases of the ANNs were updated. Findings show that the lowest averaged RMSEP was achieved by the proposed PCs-AANN and AANN in the corn and wheat datasets, respectively. Particularly, the computation cost of having a calibration model in secondary instruments has been substantially reduced by means of the proposed AANN algorithm.

Model development and update of portable NIRS instrument for assessment of internal quality attributes of two navel orange varieties.

The variation of fruit among batches influences the performance of the portable near infrared spectroscopy (NIRS) instrument and then determines the success or failure for practical application in fruit industry. Model development and update methods were investigated for determining soluble solids contents (SSC) and titrable acidity (TA) of navel orange. The pretreatment and variable selection methods were explored for building partial least square regression (PLSR) models. The best models, developed by the combination of second derivative (2D) and variable sorting for normalization (VSN), could predict SSC but not TA. The root mean square error of prediction (RMSEP), coefficient of determination for prediction () and ratio of prediction to deviation (RPD) for SSC were 0.66 °Brix, 0.66 and 1.73. Model maintain methods of model update (MU) and slope and bias correction (SBC) achieved the best results in predicting SSC for two external validation sets with , RMSEP and RPD of 0.54, 0.83 °Brix, 1.60 and 0.52, 0.83 °Brix, 1.65, respectively. The results suggested model development and update with MU and SBC could improve the robustness of the portable NIRS instrument in predicting SSC of navel orange.

Comparison of Vibrational Spectroscopic Techniques for Quantification of Water in Natural Deep Eutectic Solvents.

Vibrational spectroscopic techniques, i.e., attenuated total reflectance infrared (ATR-IR), near infrared spectroscopy (NIRS) and Raman spectroscopy (RS), coupled with Partial Least Squares Regression (PLSR), were evaluated as cost-effective label-free and reagent-free tools to monitor water content in Levulinic Acid/L-Proline (LALP) (2:1, mol/mol) Natural Deep Eutectic Solvent (NADES). ATR-IR delivered the best outcome of Root Mean Squared Error (RMSE) of Cross-Validation (CV) = 0.27% added water concentration, RMSE of Prediction (P) = 0.27% added water concentration and mean % relative error = 2.59%. Two NIRS instruments (benchtop and handheld) were also compared during the study, respectively yielding RMSECV = 0.35% added water concentration, RMSEP = 0.56% added water concentration and mean % relative error = 5.13% added water concentration, and RMECV = 0.36% added water concentration, RMSEP = 0.68% added water concentration and mean % relative error = 6.23%. RS analysis performed in quartz cuvettes enabled accurate water quantification with RMECV = 0.43% added water concentration, RMSEP = 0.67% added water concentration and mean % relative error = 6.75%. While the vibrational spectroscopic techniques studied have shown high performance in relation to reliable determination of water concentration, their accuracy is most likely related to their sensitivity to detect the LALP compounds in the NADES. For instance, whereas ATR-IR spectra display strong features from water, Levulinic Acid and L-Proline that contribute to the PLSR predictive models constructed, NIRS and RS spectra are respectively dominated by either water or LALP compounds, representing partial molecular information and moderate accuracy compared to ATR-IR. However, while ATR-IR instruments are common in chemistry and physics laboratories, making the technique readily transferable to water quantification in NADES, Raman spectroscopy offers promising potential for future development for in situ, sample withdrawal-free analysis for high throughput and online monitoring.

Estimation of crude protein and amino acid contents in whole, ground and defatted ground soybeans by different types of near-infrared (NIR) reflectance spectroscopy

This study was designed to examine the ability of near-infrared (NIR) spectroscopy to determine amino acid and crude protein concentrations in soybean and explore the effects of particle size, fat content and types of NIR spectroscopy instruments on the predictive ability of the NIR models. Whole, ground and defatted ground soybean were scanned by a diode array NIR analyzer and defatted ground soybean were scanned by a Fourier transformed NIR analyzer. Samples were analyzed for crude protein and amino acid contents using reference wet chemistry methods. The NIR calibration models of crude protein and most amino acids showed acceptable coefficient of determinations (R2c = 0.605–0.952), while the NIR models for Cys, Met and Trp were less accurate (R2c = 0.498–0.667 for Cys and R2c = 0.482–0.615 for Met and R2c = 0.406–0.481 for Trp). The R2c values in NIR calibration were found to relate to the correlation between amino acids and crude protein. The grinding process and lipid extraction were found to improve the NIR spectroscopy in crude protein and amino acids prediction. The Fourier transformed NIR analyzer was found to perform similar to the diode array NIR analyzer for defatted ground soybean analysis. No significant differences were found between crude protein and amino acid contents predicted by these two NIR spectroscopy instruments (P > 0.05).

Similarities and Differences in Brain Activation Between Patients With Schizophrenia and Obsessive-Compulsive Disorder: A Near-Infrared Spectroscopy Study.

Schizophrenia (SZ) and obsessive-compulsive disorder (OCD) share several epidemiological and clinical features, but the neurobiological substrates shared by these two diseases remain unclear. This study aimed to explore the similarities and differences in brain function between them using near-infrared spectroscopy (NIRS). Eventually, 130 SZ patients, 70 OCD and 75 normal controls (NCs) were enrolled. A 52-channel NIRS instrument was used to detect the concentration changes in oxygenated hemoglobin ([oxy-Hb]) during the verbal fluency task. Ten regions of interests (ROIs) were defined: the bilateral dorsolateral prefrontal cortex (DLPFC), frontopolar cortex (FPC), orbitofrontal cortex (OFC), inferior prefrontal gyrus (IFG) and temporal gyrus (TG). Through two different analysis strategies based on channels or ROIs, we compared the [oxy-Hb] changes in three groups by one-way analysis of variance (ANOVA) and post-hoc tests. Across 52 channels, compared to the NC group, both SZ and OCD groups exhibited reduced activity in 17 channels, including left FPC, left DLPFC, bilateral OFC, IFG, middle TG, supplementary motor cortex and Broca’s area, while SZ showed lower activity in channel 35 (right OFC) than OCD patients. Across all ROIs, compared to the NC group, both SZ and OCD groups showed reduced activity in 7 ROIs, including left FPC, bilateral OFC, IFG and TG, while SZ showed lower activity in the right OFC than OCD group, which were almost consistent with the results based on channels. This study suggests SZ and OCD present with some similar neuropathological changes, while SZ shows more severe impairment in the right OFC than OCD.

Near-Infrared Sensors for Onsite and Noninvasive Quantification of Macronutrients in Breast Milk.

Breast milk is an optimal food that covers all the nutritional needs of the newborn. It is a dynamic fluid whose composition varies with lactation period. The neonatal units of hospitals have human milk banks, a service that analyzes, stores, and distributes donated human milk. This milk is used to feed premature infants (born before 32 weeks of gestation or weighing less than 1500 g) whose mothers, for some reason, cannot feed them with their own milk. Here, we aimed to develop near-infrared spectroscopy (NIRS) measures for the analysis of breast milk. For this purpose, we used a portable NIRS instrument scanning in the range of 1396–2396 nm to collect the spectra of milk samples. Then, different chemometrics were calculated to develop 18 calibration models with and without using derivatives and the standard normal variate. Once the calibration models were developed, the best treatments were selected according to the correlation coefficients (r2) and prediction errors (SECVs). The best results for the assayed macronutrients were obtained when no pre-treatment was applied to the NIR spectra of fat (r2 = 0.841, SECV = 0.51), raw protein (r2 = 0.512, SECV = 0.21), and carbohydrates (r2 = 0.741, SECV = 1.35). SNV plus the first derivative was applied to obtain satisfactory results for energy (r2 = 0.830, SECV = 9.60) quantification. The interpretation of the obtained results showed the richness of the NIRS spectra; moreover, the presence of specific bands for fat provided excellent statistics in quantitative models. These results demonstrated the ability of portable NIRS sensors in a methodology developed for the quality control of macronutrients in breast milk.

Prediction of the intramuscular fat and protein content of freeze‐dried ground meat from cattle and sheep using near‐infrared spectroscopy (NIRS)

SummaryThis study assessed the ability of near‐infrared spectroscopy to predict intramuscular fat (IMF) and protein content of freeze‐dried ground beef and sheep meat determined using the modified Soxhlet extraction and Dumas combustion, and ascertain if robust calibration models could be developed for both assays using the combined spectral data from both species. Samples were sourced from sheep and cattle slaughtered for a variety of experiments, which covered a range in age, diet fed and carcase cut. All samples were scanned using a benchtop NIRS instrument to develop calibration models, a subset of which was used for later validation. Intramuscular fat and protein content of samples ranged from 5.1 to 32.8 and 63.4 to 89.8, respectively, with combined species of the R2 values of 98.4 and 96.5 (RMSE 0.74 and 0.76) for IMF and protein respectively. Additionally, animal age and carcase cut were found to have no significant impact on the precision of prediction models. Results from our study showed that individual species as well as combined calibration models can be successfully developed for predicting IMF and protein content of freeze‐dried ground beef and sheep meat.