NIR Sensor Research Articles

Comparison of Miniaturized and Benchtop NIR Spectrophotometers for Quantifying the Fatty Acid Profile of Iberian Ham

Iberian ham is a highly valued product, and considerable efforts have been made to characterize it quickly and accurately. In this scenario, portable NIR devices could provide an effective solution for the assessment of its attributes. However, the calibration quality of NIR equipment is directly influenced by the relevance of the used spectral region. Therefore, this study aims to evaluate the suitability of different NIR spectrometers, including four portable and one benchtop instrument, with varying spectral working ranges for quantifying the fatty acid composition of Iberian ham. Spectral measurements were carried out on both the muscle and the fat of the ham slices. The results showed that 24 equations with an RSQ > 0.5 were obtained for both the muscle and fat for the NIRFlex N-500 benchtop instrument, while 19 and 14 equations were obtained in the muscle and 16 and 10 equations in the fat for the Enterprise Sensor and MicroNIR, respectively. In general, more fatty acids could be calibrated when the spectra were taken from lean meat, except with the SCiO Sensor. Measurements performed in the lean and fat zones delivered complementary information. These initial findings indicate the suitability of using miniaturized NIR sensors, which are faster, are less expensive, and enable on-site measurements, for analyzing fatty acids in Iberian ham.

Process analytical technology based quality assurance of API concentration and fiber diameter of electrospun amorphous solid dispersions

In this study, a novel quality assurance system was developed utilizing Process analytical technology (PAT) tools and artificial intelligence (AI). Our goal was to monitor the critical quality attributes (CQAs) like drug concentration, morphology and fiber diameter of electrospun amorphous solid dispersion (ASD) formulations with fast at-line techniques. Doxycycline-hyclate (DOX), a tetracycline-type antibiotic was used as a model drug with 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) as the matrix excipient. The water-based formulations were electrospun with high-speed electrospinning (HSES). Raman and NIR sensors and machine vision-based color measurement techniques were employed to accurately determine the drug concentration. Given that morphology can influence the solubility of the drug, a convolutional neural network (CNN)-based AI model was developed to examine this property and detect manufacturing defects. Additionally, the diameter of electrospun fibrous samples was measured using camera images and a trained AI model, enabling rapid analysis of fiber diameter with results similar to that of scanning electron microscopy (SEM). These methods and models demonstrate potential in-line analytical tools, offering rapid, cheap and non-destructive analysis of ASD formulations.

In-line NIR coupled with machine learning to predict mechanical properties and dissolution profile of PLA-Aspirin

In the production of polymeric drug delivery devices, dissolution profile and mechanical properties of the drug loaded polymeric matrix are considered important Critical Quality Attributes (CQA) for quality assurance. However, currently the industry relies on offline testing methods which are destructive, slow, labour intensive, and costly. In this work, a real-time method for predicting these CQAs in a Hot Melt Extrusion (HME) process is explored using in-line NIR and temperature sensors together with Machine Learning (ML) algorithms. The mechanical and drug dissolution properties were found to vary significantly with changes in processing conditions, highlighting that real-time methods to accurately predict product properties are highly desirable for process monitoring and optimisation. Nonlinear ML methods including Random Forest (RF), K-Nearest Neighbours (KNN) and Recursive Feature Elimination with RF (RFE-RF) outperformed commonly used linear machine learning methods. For the prediction of tensile strength RFE-RF and KNN achieved R2 values 98% and 99%, respectively. For the prediction of drug dissolution, two time points were considered with drug release at t = 6 h as a measure of the extent of burst release, and t = 96 h as a measure of sustained release. KNN and RFE-RF achieved R2 values of 97% and 96%, respectively in predicting the drug release at t = 96 h. This work for the first time reports the prediction of drug dissolution and mechanical properties of drug loaded polymer product from in-line data collected during the HME process.

A novel HER2-specific sensor based on DARPin_9–29 and albumin binding domain for real-time fluorescence-guided tumor detection in animal model of cancer

In animal models of cancer, targeted fluorescence bioimaging, performed non-invasively and in real time, is indispensable tool for assessing tumor location, spread of metastasis, and the therapeutic potential of anticancer drugs under development.To overcome the limitation of antibodies in bioimaging applications, small artificial scaffold proteins based on ankyrin repeats (DARPins, designed ankyrin repeat proteins) are used as tumor-associated antigen binders. In this study for the first time, we assessed the potential of DARPin_9–29, the human epidermal growth factor receptor 2 (HER2) subdomain I-specific protein, genetically fused with albumin binding domain (ABD) and conjugated with Cyanine5.5 as a NIR sensor for fluorescence bioimaging of HER2-positive cancer in animal model.In vivo biodistribution studies have revealed sufficient tumor-to-background ratios at 48 h (3.17 ± 0.55) and 72 h (3.49 ± 0.64) postinjection, providing excellent contrast between the primary tumor and tissue background and allowing clear breast tumor detection. Ex vivo biodistribution has shown that ABD module in DARP-ABD sensor prevents renal reabsorption and increases tumor accumulation in more than 10-folds compared to excreting organs.To verify if DARP-ABD-Cy5.5 can demarcate HER2-positive tumor in vivo, HER2-positive syngeneic breast cancer cell line with constitutive gene expression of luciferase eFFLuc, was created. The powerful combination of bioluminescence and fluorescence imaging let to track the fluorescent anti-HER2 DARP-ABD sensor in bioluminescent HER2-positive breast tumors.Our results validate DARP-ABD as a promising sensor for fluorescence-guided imaging of HER2-positive solid cancer, which can be used in the development of improved anticancer treatment strategies.

Development of a 3D Printed Liquid-Core Hydrogel Platform for Real-Time Carbon Nanotube Sensors: A Breakthrough in Minimally Invasive Health Monitoring

The need for minimally invasive, real-time, and low-cost medical sensors holds great potential to revolutionize the healthcare industry. Single-walled carbon nanotube (SWNT)-based near-infrared (nIR) sensors are a relatively new field, able to provide high-quality spatial and temporal information regarding a wide range of cellular signaling molecules, including nitric oxide (NO), and hydrogen peroxide (H2O2). However, their use for in vitro and in vivo applications has been limited due to challenges related to their compatibility with packaging or immobilization techniques. Approaches like surface immobilization on solid substrates and dialysis bag confinement are costly, unstable, and unsuitable for use in settings with limited resources. SWNT sensors have recently been implanted into a large animal model as a proof-of-concept, but the challenge of retaining SWNT is currently restricting the advancement of hydrogel technology in wearable/implantable biosensors. We have developed and optimized a 3D printed liquid-core self-healing hydrogel platform that encapsulates the (AT)15 wrapped-SWNT, a NO sensor, and studied its mechanical and optical characterizations. Notably, after 90-days at 37 °C, the fluorescence intensity exhibited a negligible decrease. Statistical analysis indicates that the change in intensity from day 1 to day 90 was statistically insignificant, highlighting the sensor's robust and stable performance over the extended testing period. A linear calibration curve was obtained when the percent quenching was plotted against the logarithm of NO concentration. Furthermore, a polyvinyl alcohol (PVA) wrapped SWNT, a non-reactive sensor, was implanted into 3D printed liquid-core hydrogel platform and no quenching was observed when it was exposed to NO. Therefore, 3D printed liquid-core hydrogel platforms offer a solution for on-site detection of small-molecule analytes, overcoming a major challenge in real-time medical diagnostics and environmental monitoring.

Implementing multiblock techniques in a full-scale plant scenario: On-line prediction of quality parameters in a continuous process for different acrylonitrile butadiene styrene (ABS) products

BackgroundThe study explores the challenges of handling multiblock data of different natures (process and NIR sensors) for on-line quality prediction in a full-scale plant scenario, namely a plant operating in continuous on an industrial scale and producing different grade Acrylonitrile Butadiene Styrene (ABS) products. This environment is an ideal scenario to evaluate the use of multiblock data analysis methods, which can enhance data interpretation, visualization, and predictive performances. In particular, a novel multiblock extension of Locally Weighted PLS has been proposed by the authors, namely Locally Weighted Multiblock Partial Least Squares (LW-MB-PLS). Response-Oriented Sequential Alternation (ROSA) has also been employed to evaluate the diverse block relevance for the prediction of two quality parameters associated with the polymer. Data are split in blocks both according to sensor type and different plant sections, and different models have been built by incremental addition of data blocks to evaluate if early estimation of product quality is feasible. ResultsROSA method showed promising predictive performance for both quality parameters, highlighting the most influential plant sections through the selection of data blocks. The results suggested that both early and late-stage sensors play crucial roles in predicting product quality. A reasonable estimation of quality parameters before production completion has been achieved. On the other hand, the proposed LW-MB-PLS, while comparable in predictive performances, allowed reducing systematic prediction errors for specific products. SignificanceThis study contributes valuable insights for continuous production processes, aiding plant operators and paving the way for advancements in online quality prediction and control. Furthermore, it is implemented as a locally weighted extension of MB-PLS.

On-farm NIR sensor for milk analysis: Exploitation of bias monitoring and bias correction

Long-term studies have shown a bias drift over time in the prediction performance of near-infrared spectroscopy measurement systems. This bias drift generally requires extra laboratory reference measurements to detect and correct for this bias. Since these reference measurements are expensive and time consuming, there is a need for advanced methodologies for bias drift monitoring and correction without the need for taking extra samples. In this study, we propose and validate a method to monitor the bias drift and two methods to tackle it. The first method requires no extra measurements and uses a modified version of Partial Least Squares Regression to estimate and correct the bias. This method is based on the assumption that the mean concentration of the predicted component remains constant over time. The second method uses regular bulk milk measurements as a reference for bias correction. This method compares the measured concentrations of the bulk milk to the volume-weighted average concentrations of individual milk samples predicted by the sensor. Any difference between the actual and calculated bulk milk composition is then used to perform a bias correction on the predictions by the sensor system. The effectiveness of these methods to improve the component prediction was evaluated on data originating from a custom-built sensor that automatically measures the NIR reflectance and transmittance spectra of raw milk on the farm. We evaluate the practical use case where models for predicting the milk composition are trained upon installation of the sensor at the farm, and later used to predict the composition of subsequent samples over a period of more than 6 months. The effectiveness of the fully unsupervised method was confirmed when the mean concentration of the milk samples remained constant, while the effectiveness reduced when this was not the case. The bulk milk correction method was effective when all relevant samples for the component were measured by the sensor and included in the analyzed bulk milk, but is less effective when samples included in the bulk which are not measured by the sensor system. When the necessary conditions are met, these methods can be used to extend the lifetime of deployed prediction models by significantly reducing the bias on the predicted values.

Nutrient quantification of livestock slurry and digestate using a low-cost handheld NIR spectrometer and multi-biomass calibration models

Real-time analysis during slurry field application can potentially ensure higher efficiency in crop fertilisation and reduce related environmental problems. Near Infrared Spectroscopy (NIRS) can predict quickly and adequately the nutrient content of the slurry but the devices are still costly. In this study, a low-cost portable device has been tested to predict total solids, total Kjeldahl nitrogen, total ammonia nitrogen, and phosphorus contents in cattle and pig slurries and digestate. Multivariate calibration models were developed for different biomass types (multi-biomass) by comparing support vector machine (SVM) and partial least square (PLS) regressions. The developed SVM and PLS models showed satisfactory and similar results, with performance to deviation ratio (RPD) values ranging between 2.22 and 2.80. The use of the handheld device with the obtained models also meets the requirements of a certification protocol for commercial NIR sensors and therefore can ensure a better slurry management at farm level.

Novel Near-Infrared-II In Vivo Visualization Revealed Rapid Calcium Intestine Turnover in Daphnia magna with Delayed Impact by Cadmium and Acidification.

Calcium is a highly demanded metal, and its transport across the intestine of Daphnia magna remains a significant unresolved question. Due to technical constraints, the visualization of the kinetic process of Ca passage through D. magna has been challenging. Here, we developed the second near-infrared Ca sensor (NIR-II Ca) and conducted real-time in vivo imaging of Ca in daphnids with a high signal-to-noise ratio, deep tissue penetration, and minimal damage. Through the utilization of the NIR-II Ca sensor, we for the first time visualized and quantified the kinetic process of Ca passage in the intestine in real time. The results revealed that trophically available Ca passed through the intestines in 24 h, whereas waterborne Ca required only 35 min. This rapid "flushing through" mechanism established waterborne Ca as the primary source of Ca absorption. However, environmental stressors such as water acidification and cadmium significantly delayed the Ca passage and absorption. The development of NIR imaging and sensors allows for real-time dynamic visualization of contaminants/nutrients in organisms and holds great potential as a powerful tool for future studies into material kinetic processes in living animals.

Multifunctional phthalocyanine NIR sensor for fluorescent and colorimetric dual-imaging and removal of intracellular and environmental Cu2+

Cu2+ sensing and removal are of great significance for environment protection and human health. Herein, multifunctional Cu2+ sensor system for both detection and removal of intracellular and environmental Cu2+ based on trans/cis interconversion of silicon phthalocyanine (SiPc) was developed. This phthalocyanine based Cu2+ sensor shows remarkably dual-mode NIR fluorescence and colorimetric responses for recognition of Cu2+, resulting in insoluble chelate for Cu2+ removal as well as the extremely high anti-interference in the presence of various anions and cations. The binding mechanism and stoichiometry ratio were systematically revealed. Intracellular fluorescence imaging in MCF-7 cells demonstrated that Cu2+ could be efficiently detected in living cells and form insoluble chelate for clearance. As the result, this novel phthalocyanine based multifunctional sensor can be used for dual-mode NIR fluorescence/colorimetric detection and removal of Cu2+ in either complicated environment or biological systems.

Evaluation of a handheld near‐infrared spectroscopy sensor for rapid corn kernel moisture estimation

Core Ideas Rapid and accurate estimation of corn kernel moisture in‐field is critical for timely harvests. The NIRS sensor predicted corn kernel moisture with high accuracy (R2 ≥ .95) across a wide moisture range (15–60%). Sensor accuracy was also consistent across multiple corn hybrids and environments.

Cerebral oxygen saturation in neonates: a bedside comparison between neonatal and adult NIRS sensors.

The majority of neonatal NIRS literature recommends target ranges for cerebral saturation (rScO2) based on data using adult sensors. Neonatal sensors are now commonly used in the neonatal intensive care unit (NICU). However, there is limited clinical data correlating these two measurements of cerebral oxygenation. A prospective observational study was conducted in two NICUs between November 2019 and May 2021. An adult sensor was placed on infants undergoing routine cerebral NIRS monitoring with a neonatal sensor. Time-synchronized rScO2 measurements from both sensors, heart rate, and systemic oxygen saturation values were collected over 6 h under varying clinical conditions and compared. Time-series data from 44 infants demonstrated higher rScO2 measurements with neonatal sensors than with adult sensors; however, the magnitude of the difference varied depending on the absolute value of rScO2 (Adult = 0.63 × Neonatal + 18.2). While there was an approximately 10% difference when adult sensors read 85%, readings were similar when adult sensors read 55%. rScO2 measured by neonatal sensors is typically higher than measured by adult sensors, but the difference is not fixed and is less at the threshold indicative of cerebral hypoxia. Assuming fixed differences between adult and neonatal sensors may lead to overdiagnosis of cerebral hypoxia. In comparison to adult sensors, neonatal sensors rScO2 readings are consistently higher, but the magnitude of the difference varies depending on the absolute value of rScO2. Marked variability during high and low rScO2 readings was noted, with approximately 10% difference when adult sensors read 85%, but nearly similar (58.8%) readings when adult sensors read 55%. Estimating fixed differences of approximately 10% between adult and neonatal probes may lead to an inaccurate diagnosis of cerebral hypoxia and result in subsequent unnecessary interventions.

Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches.

Phenomics has emerged as important tool to bridge the genotype-phenotype gap. To dissect complex traits such as highly dynamic plant growth, and quantification of its component traits over a different growth phase of plant will immensely help dissect genetic basis of biomass production. Based on RGB images, models have been developed to predict biomass recently. However, it is very challenging to find a model performing stable across experiments. In this study, we recorded RGB and NIR images of wheat germplasm and Recombinant Inbred Lines (RILs) of Raj3765xHD2329, and examined the use of multimodal images from RGB, NIR sensors and machine learning models to predict biomass and leaf area non-invasively. The image-based traits (i-Traits) containing geometric features, RGB based indices, RGB colour classes and NIR features were categorized into architectural traits and physiological traits. Total 77 i-Traits were selected for prediction of biomass and leaf area consisting of 35 architectural and 42 physiological traits. We have shown that different biomass related traits such as fresh weight, dry weight and shoot area can be predicted accurately from RGB and NIR images using 16 machine learning models. We applied the models on two consecutive years of experiments and found that measurement accuracies were similar suggesting the generalized nature of models. Results showed that all biomass-related traits could be estimated with about 90% accuracy but the performance of model BLASSO was relatively stable and high in all the traits and experiments. The R2 of BLASSO for fresh weight prediction was 0.96 (both year experiments), for dry weight prediction was 0.90 (Experiment 1) and 0.93 (Experiment 2) and for shoot area prediction 0.96 (Experiment 1) and 0.93 (Experiment 2). Also, the RMSRE of BLASSO for fresh weight prediction was 0.53 (Experiment 1) and 0.24 (Experiment 2), for dry weight prediction was 0.85 (Experiment 1) and 0.25 (Experiment 2) and for shoot area prediction 0.59 (Experiment 1) and 0.53 (Experiment 2). Based on the quantification power analysis of i-Traits, the determinants of biomass accumulation were found which contains both architectural and physiological traits. The best predictor i-Trait for fresh weight and dry weight prediction was Area_SV and for shoot area prediction was projected shoot area. These results will be helpful for identification and genetic basis dissection of major determinants of biomass accumulation and also non-invasive high throughput estimation of plant growth during different phenological stages can identify hitherto uncovered genes for biomass production and its deployment in crop improvement for breaking the yield plateau.

Oxygen Uptake and Bilaterally Measured Vastus Lateralis Muscle Oxygen Desaturation Kinetics in Well-Trained Endurance Cyclists.

The aim of the present study was to compare and analyse the relationships between pulmonary oxygen uptake and vastus lateralis (VL) muscle oxygen desaturation kinetics measured bilaterally with Moxy NIRS sensors in trained endurance athletes. To this end, 18 trained athletes (age: 42.4 ± 7.2 years, height: 1.837 ± 0.053 m, body mass: 82.4 ± 5.7 kg) visited the laboratory on two consecutive days. On the first day, an incremental test was performed to determine the power values for the gas exchange threshold, the ventilatory threshold (VT), and V̇O2max levels from pulmonary ventilation. On the second day, the athletes performed a constant work rate (CWR) test at the power corresponding to the VT. During the CWR test, the pulmonary ventilation characteristics, left and right VL muscle O2 desaturation (DeSmO2), and pedalling power were continuously recorded, and the average signal of both legs' DeSmO2 was computed. Statistical significance was set at p ≤ 0.05. The relative response amplitudes of the primary and slow components of VL desaturation and pulmonary oxygen uptake kinetics did not differ, and the primary amplitude of muscle desaturation kinetics was strongly associated with the initial response rate of oxygen uptake. Compared with pulmonary O2 kinetics, the primary response time of the muscle desaturation kinetics was shorter, and the slow component started earlier. There was good agreement between the time delays of the slow components describing global and local metabolic processes. Nevertheless, there was a low level of agreement between contralateral desaturation kinetic variables. The averaged DeSmO2 signal of the two sides of the body represented the oxygen kinetics more precisely than the right- or left-leg signals separately.

Preterm infants variability in cerebral near-infrared spectroscopy measurements in the first 72-h after birth.

Cerebral near-infrared spectroscopy is a non-invasive tool used to measure regional cerebral tissue oxygenation (rScO2) initially validated in adult and pediatric populations. Preterm neonates, vulnerable to neurologic injury, are attractive candidates for NIRS monitoring; however, normative data and the brain regions measured by the current technology have not yet been established for this population. This study's aim was to analyze continuous rScO2 readings within the first 6-72 h after birth in 60 neonates without intracerebral hemorrhage born at ≤1250 g and/or ≤30 weeks' gestational age (GA) to better understand the role of head circumference (HC) and brain regions measured. Using a standardized brain MRI atlas, we determined that rScO2 in infants with smaller HCs likely measures the ventricular spaces. GA is linearly correlated, and HC is non-linearly correlated, with rScO2 readings. For HC, we infer that rScO2 is lower in infants with smaller HCs due to measuring the ventricular spaces, with values increasing in the smallest HCs as the deep cerebral structures are reached. Clinicians should be aware that in preterm infants with small HCs, rScO2 displayed may reflect readings from the ventricular spaces and deep cerebral tissue. Clinicians should be aware that in preterm infants with small head circumferences, cerebral near-infrared spectroscopy readings of rScO2 displayed may reflect readings from the ventricular spaces and deep cerebral tissue. This highlights the importance of rigorously re-validating technologies before extrapolating them to different populations. Standard rScO2 trajectories should only be established after determining whether the mathematical models used in NIRS equipment are appropriate in premature infants and the brain region(s) NIRS sensors captures in this population, including the influence of both gestational age and head circumference.

Imaging Sensor-Based High-Throughput Measurement of Biomass Using Machine Learning Models in Rice

Phenomics technologies have advanced rapidly in the recent past for precision phenotyping of diverse crop plants. High-throughput phenotyping using imaging sensors has been proven to fetch more informative data from a large population of genotypes than the traditional destructive phenotyping methodologies. It provides accurate, high-dimensional phenome-wide big data at an ultra-super spatial and temporal resolution. Biomass is an important plant phenotypic trait that can reflect the agronomic performance of crop plants in terms of growth and yield. Several image-derived features such as area, projected shoot area, projected shoot area with height constant, estimated bio-volume, etc., and machine learning models (single or multivariate analysis) are reported in the literature for use in the non-invasive prediction of biomass in diverse crop plants. However, no studies have reported the best suitable image-derived features for accurate biomass prediction, particularly for fully grown rice plants (70DAS). In this present study, we analyzed a subset of rice recombinant inbred lines (RILs) which were developed from a cross between rice varieties BVD109 × IR20 and grown in sufficient (control) and deficient soil nitrogen (N stress) conditions. Images of plants were acquired using three different sensors (RGB, IR, and NIR) just before destructive plant sampling for the quantitative estimation of fresh (FW) and dry weight (DW). A total of 67 image-derived traits were extracted and classified into four groups, viz., geometric-, color-, IR- and NIR-related traits. We identified a multimodal trait feature, the ratio of PSA and NIR grey intensity as estimated from RGB and NIR sensors, as a novel trait for predicting biomass in rice. Among the 16 machine learning models tested for predicting biomass, the Bayesian regularized neural network (BRNN) model showed the maximum predictive power (R2 = 0.96 and 0.95 for FW and DW of biomass, respectively) with the lowest prediction error (RMSE and bias value) in both control and N stress environments. Thus, biomass can be accurately predicted by measuring novel image-based parameters and neural network-based machine learning models in rice.

Miniaturized NIR Spectrometers in a Nutshell: Shining Light over Sources of Variance

The increasing portability and accessibility of miniaturized NIR spectrometers are promoting the spread of in-field and online applications. Alongside the successful outcomes, there are also several problems related to the acquisition strategies for each instrument and to experimental factors that can influence the collected signals. An insightful investigation of such factors is necessary and could lead to advancements in experimental set-up and data modelling. This work aimed to identify variation sources when using miniaturized NIR sensors and to propose a methodology to investigate such sources based on a multivariate method (ANOVA—Simultaneous Component Analysis) that considers the effects and interactions between them. Five different spectrometers were chosen for their different spectroscopic range and technical characteristics, and samples of worldwide interest were chosen as the case study. Comparing various portable sensors is interesting since results could significantly vary in the same application, justifying the idea that this kind of spectrometer is not to be treated as a general class of instruments.

Estimation of Acetic Acid Concentration from Biogas Samples Using Machine Learning

In a biogas plant, the acetic acid concentration is a major component of the substrate as it determines the pH value, and this pH value correlates with the volume of biogas produced. Since it requires specialized laboratory equipment, the concentration of acetic acid in a biogas substrate cannot be measured on-line. The project aims to use NIR sensors and machine learning algorithms to estimate the acetic acid concentration in a biogas substrate based on the measured intensities of the substrate. As a result of this project, it was possible to determine whether the acetic acid concentration in a biogas substrate is higher or lower than 2 g/l using machine learning models.

On the Importance of Investigating Data Structure in Miniaturized NIR Spectroscopy Measurements of Food: The Case Study of Sugar.

Alongside the increasing proofs of efficacy of miniaturized NIR instruments in food-related scenarios, it is progressively growing the number of end-users, even incentivized by the low-cost of the sensors. While attention is paid to the analytical protocol-from sampling to data collection, up to the data processing, the importance of error investigation in raw data is generally underestimated. Understanding the sources and the structure of uncertainty related to the raw data improves the quality of measurements and suggests the correct planning of the experiments, as well as helps in chemometric model development. The goal of chemometric modeling is to separate information from noise; therefore, a description of the nature of measurement error structure is necessary. Among the different approaches, we present the study of the Error Covariance Matrices (ECMs) and their decomposition in a bilinear structure as a powerful method to study the main sources of variability when using miniaturized NIR sensors in the actual way of use. Granulated and lump sugar samples were chosen as the case study and analyzed with two miniaturized spectrometers working in the NIR regions around 1350-2550 nm and 900-1750 nm, respectively, in dispersive reflectance mode. Results show that having some insights on multivariate measurement errors associated with spectra could be interesting in paving the way for several applications.

Nondestructive detection of S‐ovalbumin content in eggs using portable NIR spectrometer and MPA‐CARS

AbstractThe feasibility of applying a low‐cost and portable NIR spectrometer to detect the S‐ovalbumin content of eggs was investigated in this study, and a model population analysis based competitive adaptive reweighted sampling (MPA‐CARS) algorithm was proposed to reduce the dimensionality of spectral data. The NIR spectra of egg samples with different storage periods were collected in the wavelength range of 900–1700 nm. Standard normal variate (SNV) and Savitzky–Golay1st derivative were used to preprocess the raw spectral data. Then, CARS and the proposed MPA‐CARS were applied to select effective wavelengths from the whole spectral range. Statistical results showed that MPA‐CARS had better feature extraction performance than CARS and the number of selected feature wavelengths was less. Support vector machine (SVM), back propagation neural network (BPNN) and extreme gradient boosting (XGBoost) were used to establish calibration models for predicting S‐ovalbumin content, in which the simplified XGBoost model based on MPA‐CARS feature wavelengths yielded the best performance, with R2P of 0.906 and RMSEP of 7.799%. Therefore, portable NIR has the potential to be a useful tool for S‐ovalbumin content detection. This could help food processing industry to arrange miniaturized NIR sensors to detect egg quality at different points in the egg supply chain.Practical applicationsS‐ovalbumin content is a freshness index of eggs and can affect the quality of processed foods. This study demonstrated the feasibility of using portable NIR spectrometer to detect the S‐ovalbumin content of eggs, which can help food processing agencies to implement low‐cost NIR sensors to detect egg quality at different stages of the supply chain.