NIR Method Research Articles

Geographic traceability of Gastrodia elata Blum based on combination of NIRS and Chemometrics

The content of the active ingredient in G. elata Bl. is affected by the soil and climate of different regions, so geographical traceability is essential to ensure its quality, commercial value. This study used a combination of NIRS and various chemometric methods to establish an effective geotraceability method for G. elata Bl.. Firstly, a traditional machine learning model was built based on the SF dataset NIRS, and a ResNet model was built based on NIRS generated 2DCOS images and 3DCOS images. Secondly, the model performance was validated using the ZT dataset. The results show that the 3DCOS-ResNet model performs the best with 100.00 % and 95.45 % test set and EV accuracy, respectively. This study provides a theoretical basis for regulators to quickly ensure the authenticity of G. elata Bl. sources. However, more data and in-depth studies are needed in the future to validate and improve the applicability of the model.

Muscle Mitochondrial Capacity Is Impaired Immediately Following Maximal Exercise.

This study measured the time course mVO2max following both maximal and submaximal exercise. Healthy male and female participants were tested (n = 12 maximal and n = 8 submaximal exercise). A NIRS device was placed on the left medial gastrocnemius. Participants performed either one minute of maximal, rapid (~2 Hz), or submaximal (~0.37 Hz) plantar flexion exercise on a custom pneumatic ergometer. mVO2max was measured before and immediately after exercise. mVO2max measurements consisted of four incomplete recovery curves of muscle metabolism taken after 30 seconds of electrical muscle stimulation except in the first post-exercise trial. The four recovery curves were collected 50-, 156-, 260-, and 366-seconds post-exercise, each producing a mVO2max rate constant. After maximal exercise muscle acceleration decreased to 52 + 18% (p = 0.001) of pre-values. mVO2max was reduced from the pre-exercise mean at the first post-trial (2.16 + 0.44 min-1 to 1.21 + 0.52 min -1, p < 0.001). The fourth trial showed recovery from the first (2.2 + 0.46 min-1 vs 1.21 + 0.52 min-1, p < 0.001) and was not significantly different from pre-exercise values (2.2 + 0.46 min-1 vs 2.16 + 0.44 min-1, p = 0.41). No change in acceleration or mVO2max was seen after submaximal exercise (p > 0.05). The 56.7% reduction in mVO2max supports the hypothesis that in young, healthy individuals a minute of maximal exercise transiently impairs mVO2max which then recovers within six minutes. The NIRS method shows promise in tracking time course changes in mVO2max and warrants further investigation of the transient effects of exercise on mVO2max.

NIR Spectroscopy application to determine the carbohydrate and lipid content in green bean coffee

Coffee flavor is influenced by the chemical concentration including the carbohydrate and lipid content. Determination of the chemical content is still using the chemical method that spend time, expensive and destructive, then a suitable method which is fast and non destructive is required especially to fulfill the needs of coffee industries. This study aimed to apply NIRS method to determine the carbohydrate and lipid content from Indonesia green bean coffee from several origin to increase the data distribution, NIR wave of 1000-2500 nm, followed by determination of the carbohydrate and lipid content using chemical method. After that, NIR data pretreatment using multiple scatter correction (MSC), first and second derivative of savitzky golay, normalization and the combinations then calibrate both the spectra and chemical data by Partial Least Square (PLS) method. Result showed that NIR spectroscopy can be used to determine the carbohydrate and lipid content in green bean coffee could be seen by the high coefficient correlation, RPD and consistency value, while the CV is lower than 2.

Detection of Formalin Content in Chicken Meat Using Portable Near Infrared Spectrometer

Food safety is essential for consumers. One of the compound that are prohibited from being used to preserve chicken meat is formalin. It demands a fast classification and sorting process for chicken meat, whether it is processed further or not. The main objective of this research is to develop a model that can predict the formalin content of chicken meat at room temperature using a portable NIR spectrometer. The NIRS method utilizes electromagnetic waves in infrared radiation with wavelengths ranging from 740-1070 nm. The method used to process the data is partial least square discriminative analysis (PLS-DA) to determine the presence of formalin in chicken meat. The results showed that the best pre-treatment was using the 1st derivative which had calibration results with an accuracy value of 92.86%, sensitivity 94.05%, specificity 91.67%, and FPR 8.33%. While the validation results obtained an accuracy value of 92.86%, sensitivity 92.86%, specificity 92.86%, and FPR 7.14%. Keywords: Chicken meat, formalin, NIRS, non-destructive method, PLS-DA.

Light and Shadow in Near-Infrared Spectroscopy: A Powerful Tool for Cannabis sativa L. Analysis

Cannabis sativa L. is an ancient cultivar that has found applications in various fields, e.g., medicine, due to its beneficial effects. However, due to its psychotropic effects, the regulation of this cultivar has increased throughout the decades. In this context, the need for rapid and reliable analytical methods to ensure the quality control of Cannabis cultivars has become of extreme importance. NIRS has arisen as a powerful tool in this field due to its multiple advantages, e.g., non-destructive, rapid, and cost-effective. In this article, the chemometric techniques commonly employed in NIRS method development are described, along with their application for the analysis of Cannabis samples. Regarding qualitative methods, different mathematical treatments and classification models are explained. As for quantitative methods, the representative linear and non-linear modelling techniques applied for the development of prediction equations are described, alongside their application in the Cannabis field. To the best of our knowledge, this is the first time this type of review is written, since there are several articles which address cannabinoid determination, but the main purpose of this review is to enhance the potential of NIRS over the traditional techniques employed for the analysis of Cannabis samples.

A novel transfer method with neural network architecture searching to predict asymptomatic of “akizuki” pear cork spot disorder on near-infrared spectroscopy

“Akizuki” pear cork spot disorder is a physiological disease, the ability to effectively diagnose the diseased fruit can directly affect the fruit quality. To improve the diagnostic performance of asymptomatic samples, this paper proposes a novel spectral transfer modelling method based on neural network architecture searching, named TranNAS_NIR. The experiments show that the model by the proposed TranNAS_NIR method is more effective. The accuracy of the target domain test set reaches 82.61%, which is 5.3% better than the model with transfer component analysis (TCA) and 10.5% better than the model constructed only relying on the target domain data and without the transfer learning method. The novel transfer method with neural network architecture search proposed in this paper for the asymptomatic prediction model of “Akizuki” pear cork spot disorder also further provides a theoretical basis for the NIR model improvement of other asymptomatic diseases.

Assessment of Intercostal Muscle Near-Infrared Spectroscopy for Estimating Respiratory Compensation Point in Trained Endurance Athletes.

This study aimed to assess the reliability and validity of estimating the respiratory compensation point (RCP) in trained endurance athletes by analyzing intercostal muscles' NIRS-derived tissue oxygenation dynamics. Seventeen experienced trail runners underwent an incremental treadmill protocol on two separate occasions, with a 7-day gap between assessments. Gas exchange and muscle oxygenation data were collected, and the oxygen saturation breakpoint (SmO2BP) measured in the intercostal muscles was compared to the RCP, which was identified by the increase in the VE/V.CO2 slope and the point at which the PetCO2 started to decrease. No statistically significant differences were observed between the two methods for any of the variables analyzed. Bland-Altman analysis showed significant agreement between the NIRS and gas analyzer methods for speed (r = 0.96, p < 0.05), HR (r = 0.98, p < 0.05), V.O2 relative to body mass (r = 0.99, p < 0.05), and %SmO2 (r = 0.98, p < 0.05). The interclass correlation coefficient values showed moderate to good reliability (0.60 to 0.86), and test-retest analysis revealed mean differences within the confidence intervals for all variables. These findings suggest that the SmO2BP measured using a portable NIRS device in the intercostal muscles is a reliable and valid tool for estimating the RCP for experienced trail runners and might be useful for coaches and athletes to monitor endurance training.

SELECTION OF SOYBEAN LINES BASED ON A NUTRACEUTICAL IDEOTYPE

This study aims to select soybean lines with desirable nutraceutical characteristics under specific meteorological conditions in the selection environments. The experiment was conducted during the 2021/2022 harvest at Escola Fazenda, Regional University of the Northwest of the State of Rio Grande do Sul, located in Augusto Pestana- RS, Brazil. The experimental design was augmented blocks, with 27 populations of F2 generation, 118 lines of F5 generation, 70 lines of F8 generation, and 15 soybean cultivars as interim checks, arranged in three replications. The chemical composition of soybeans was evaluated using Near Infrared Spectroscopy - NIRS method to analyze various nutritional components. The IRC050 genotype exhibited a nutraceutical profile characterized by an increment in stearic fatty acid (STEAR) and reduction in fiber (FB) and mineral material (MM) contents. Among the F5 generation, the following genotypes were selected based on their nutraceutical traits: L174F5, L307F5, L322F5, L205F5, L190F5, L196F5, L219F5, L183F5, L301F5, L172F5, L198F5, L182F5, L319F5, L21F5, L188F5, L217F5, L302F5, L315F5, L36F5, L318F5, L211F5, L165F5, L321F5, and L179F5. The indirect selection of genotypes with higher levels of linoleic acid and protein was facilitated using the enzyme peroxidase.

Silage maturity of maize in a foothill area with NIRS method support – part II

Silage maturity of maize in a foothill area with NIRS method support – part II

Rapid Prediction of Leaf Water Content in Eucalypt Leaves Using a Handheld NIRS Instrument

Leaf water content (LWC) is a crucial physiological parameter that plays a limiting role in the efficiency of photosynthesis and biomass production in many plants. This study investigated the use of diffuse reflectance near-infrared spectroscopy (NIRS) for the rapid prediction of the gravimetric LWC in eucalypt leaves from Eucalyptus and Corymbia genera. The best-performing model for LWC gave a R2pred of 0.85 and RMSEP of 2.32% for an independent test set, indicating that the handheld NIR instrument could predict the LWC with a high level of accuracy. The use of support vector regression gave slightly more accurate results compared with partial least squares regression. Prediction models were also developed for leaf thickness, although these were somewhat less accurate (R2pred of 0.58; RMSEP of 2.7 µm). Nevertheless, the results suggest that handheld NIR instruments may be useful for in-field screening of LWC and leaf thickness in Australian eucalypt species. As an example of its use, the NIR method was applied for rapid analysis of the LWC and leaf thickness of every leaf found on an E. populnea sapling.

Optical characteristics of fermented cocoa pod husk based on Nears Infrared Reflectance Spectroscopy and evaluation of its quality as animal feed materials

Near Infrared Spectroscopy (NIRs) is one of the alternative methods to determine chemical characteristic of the animal feed due to its several benefits. The purpose of this study was to determine optical characteristics and the quality of fermented cacao pod husk as animal feed by NIRs method. Totally eighteen samples of fermented cocoa pod husk were analyzed by wet analysis consisting of as dry matter, crude protein, crude fiber, crude fat, crude ash, and nitrogen-free extract material. Spectrum acquisition was carried out using a Thermo Nicolet Antaris II tool. Chemical analysis is intended to compare the data from chemical analysis and the NIRS method. Spectrum data were processed using partial least squares (PLS) with multiplicative scatter correction (MSC) and de-Trending (DT) pre-treatment methods. The results of the study indicated that the NIRS was good prediction for dry matter, crude protein, and crude fat with the model of MSC pre-treatment. The crude ash parameter is only able to predict well on the MSC pre-treatment method with evaluation values. In conclusion, NIRS can be applied to determine animal nutritive values by means of using partial least squares (PLS), principal component regression (PCR) and other regression approaches.

Machine Learning-Enabled NIR Spectroscopy in Assessing Powder Blend Uniformity: Clear-Up Disparities and Biases Induced by Physical Artefacts

NIR spectroscopy is a non-destructive characterization tool for the blend uniformity (BU) assessment. However, NIR spectra of powder blends often contain overlapping physical and chemical information of the samples. Deconvoluting the information related to chemical properties from that associated with the physical effects is one of the major objectives of this work. We achieve this aim in two ways. Firstly, we identified various sources of variability that might affect the BU results. Secondly, we leverage the machine learning-based sophisticated data analytics processes. To accomplish the aforementioned objectives, calibration samples of amlodipine as an active pharmaceutical ingredient (API) with the concentrations ranging between 67 and 133% w/w (dose ~ 3.6% w/w), in powder blends containing excipients, were prepared using a gravimetric approach and assessed using NIR spectroscopic analysis, followed by HPLC measurements. The bias in NIR results was investigated by employing data quality metrics (DQM) and bias-variance decomposition (BVD). To overcome the bias, the clustered regression (non-parametric and linear) was applied. We assessed the model’s performance by employing the hold-out and k-fold internal cross-validation (CV). NIR-based blend homogeneity with low mean absolute error and an interval estimates of 0.674 (mean) ± 0.218 (standard deviation) w/w was established. Additionally, bootstrapping-based CV was leveraged as part of the NIR method lifecycle management that demonstrated the mean absolute error (MAE) of BU ± 3.5% w/w and BU ± 1.5% w/w for model generalizability and model transferability, respectively. A workflow integrating machine learning to NIR spectral analysis was established and implemented.Graphical Impact of various data learning approaches on NIR spectral data

Risk stratification of sudden cardiac death in subjects with asymptomatic ventricular preexcitation: diagnostic value of non-invasive and invasive methods

Abstract Introduction In subjects with asymptomatic ventricular preexcitation (AVP) guidelines recommend follow-up by non-invasive methods. However, in some patients accessory pathways (APs) show high-risk invasive parameters and risk of sudden death. Objectives To assess the association between the non-invasive and invasive risk stratification (NIRS and IRS) in AVP patients in our center. Material and methods Retrospective and observational study in 58 patients with AVP, underwent NIRS and IRS. Association between NIRS (electrocardiogram, holter and ergometry testing) and IRS variables (AERP ≤250 msec, SPERRI ≤250 msec, atrioventricular reentrant tachycardia or AF, multiple APs) from the electrophysiological study (EPS), and sensitivity and specificity of NIRS to predict a high-risk EPS were determined. Results Of patients with high-risk EPS (n=41), 55% had a previously low-risk NIRS (n=11) while 78% (n=30) had a high-risk NIRS (p=0.05). A low-risk NIRS had a sensitivity (S) 27% and specificity (E) 47% to predict a high-risk EPS. Conclusions In our population, 1 of every 2 patients with low-risk NIRS presented a high-risk IRS. The NIRS methods present low S and E for risk stratification in AVP patients. Funding Acknowledgement Type of funding sources: None.

A Comparative Study of Benchtop and Portable NIR and Raman Spectroscopic Methods for the Quantitative Determination of Curcuminoids in Turmeric Powder

Turmeric consumption is continually increasing worldwide. Curcuminoids are major active constituents in turmeric and are associated with numerous health benefits. A combination of spectroscopic methods and chemometrics shows the suitability of turmeric for food quality control due to advantages such as speed, versatility, portability, and no need for sample preparation. Five calibration models to quantify curcuminoids in turmeric were proposed using benchtop and portable devices. The most remarkable results showed that Raman and NIR calibration models present an excellent performance reporting RMSEP of 0.44% w/w and 0.41% w/w, respectively. In addition, the five proposed methods (FT-IR, Raman, and NIR) were compared in terms of precision and accuracy. The results showed that benchtop and portable methods were in good agreement and that there are no significant differences between them. This study aims to foster the use of portable devices for food quality control in situ by demonstrating their suitability for the purpose.

Towards a fast and generalized microplastic quantification method in soil using terahertz spectroscopy

Extensive investigation of microplastic abundance in soil environment calls for rapid, accurate, efficient and harmonized quantification methods. Development of rapid quantification method requires made-to-measure soil samples with additions of standard polymers. Existing rapid quantification methods ignore the gap between standard polymers in laboratory and household microplastics in soil environment. Here, terahertz (0.6–1.67 thz) and NIR (950–1660 nm) spectroscopy were compared to explore a fast, accurate and potentially generalizable microplastic quantification method in soil. Soil sample was spiked with two standard polymers (polyvinyl chloride (PVC) and polystyrene (PS)) and their additive-containing household microplastics. Two standard sample sets and two household sample sets were prepared in concentrations ranging from 0.5 to 10%. Nine commonly used preprocessing methods and three machine learning algorithms were coupled to develop methods. Models were constructed by training sets from standard sample sets. When models transferred to household samples, prediction error (RMSE) of proposed terahertz method (Wdenosie_PLSR) only increased by 0.4% for PVC and 0.19% for PS, yet that of the NIR method increased by 1.49% and 1.16% respectively. The proposed terahertz method presented a detection limit around 1.12% and the NIR method showed a detection limit around 3.24%. Overall, our results suggest that compared with NIR method, the proposed terahertz method is not only more accurate but also demonstrate stronger generalizability to bridge the gaps between standard PVC/PS polymers and household PVC/PS microplastics. We also propose MMD heatmap for diagnosing spectral preprocessing methods to further improve method efficiency.

Commercial scale transfer of a twin-screw melt granulation process for high drug load fevipiprant tablets

Objective This work summarizes select methodology of twin-screw melt granulation and process analytical technology that were used in the successful scaling-up and commercial transfer of high drug load (80.5% w/w) immediate release fevipiprant tablets. Significance The unique and compelling learnings from this industry work are (1) insights into Novartis AG’s commercial scale transfer using twin-screw melt granulation (2) rapid, non-destructive NIR methodology as a PAT tool for RTR testing. No prior literature combines these two aspects at the level of detail we present/disclose. Methods Scaling up of twin-screw melt granulation (TSMG) was guided by specific energy values obtained for the 27 mm (pilot scale) and 50 mm (commercial scale) twin-screw extruders (TSE). Proven acceptable ranges (PAR) were confirmed by varying the critical process parameters (CPPs) for granulation (screw speed) and tableting (dwell time, crushing strength) at three process levels (upper, target, lower). An at-line NIR method was developed and validated for real-time release testing. Results The combination of CPPs were selected to have the same effect on critical quality attributes (CQAs) i.e., lower (-) and upper (+) process level challenged tablet aspect/appearance and dissolution, respectively. TSMG was performed using a 50 mm extruder at constant feed rate. Compression of the six final blends (∼300 kg) showed no impact of varied granulation and compression process conditions on both CQAs. A near-infrared spectroscopy method was validated to determine content uniformity, assay, identity, and to predict CQAs on uncoated tablets in preparation for a real time release testing (RTRT) of future batches.

Application of Online NIR Spectroscopy to Enhance Process Understanding and Enable In-process Control Testing of Secondary Drying Process for a Spray-dried Solid Dispersion Intermediate

Near infrared spectroscopy (NIRS) was utilized to determine the endpoint of secondary drying process (post primary spray drying) of Spray-Dried Intermediates (SDI). In addition, NIR methods have been developed to quantify residual solvents (acetone and water), which are in-process controls (IPCs), and assay on the spray dried intermediate, thereby minimizing the need for off-line sample testing. NIRS calibration models were built with Partial Least Squares (PLS) regression for samples from several statistically designed experiments. Standard errors of prediction (SEP) of 0.1 wt. % for acetone, 0.2 wt. % for water, and 3.0 mg API/g SDI for API potency were obtained from validation of the models. When these methods were transferred to commercial scale on a different analyzer at a different site, additional updates to the NIR models were successfully made to overcome the impact from the differences in instrumentation and scale. Not only could real-time, in-process release be achieved, but also consistency and quality of data could be improved by minimizing or eliminating sample handling issues for off-line sample analysis. These NIR methods for secondary drying might also be used to optimize the drying process cycle time and study the effect of agitation rate, jacket temperature, and drying gas sweep rate on drying cycle time.

Development of Analytical Methods for Determination of β-Carotene in Pumpkin (Cucurbita maxima) Flesh, Peel, and Seed Powder Samples.

Vegetables are consumed worldwide in fresh as well as processed forms. Pumpkin is considered as an important vegetable due to its nutritional values. The objective of this study was to evaluate all the analytical parameters and improve the performance of the methods for the determination of β-carotene in pumpkin flesh, peel, and seed samples using UV-VIS, NIR, and FTIR methods. β-Carotene was measured at 453 nm using the UV-VIS method which showed linear range, 0.1 to 12 µg/mL; R2, 0.999; LOD, 0.034 µg/mL; LOQ, 0.1 µg/mL; RSD, 1.5% to 11%; and percent recovery, 83% to 93%. β-Carotene was also measured at 1415 nm using NIR and at 1710 cm−1 using FTIR spectroscopic methods. The NIR method exhibited linear range, 12.5 to 250 µg/mL; R2, 0.999; LOD, 3.4 µg/mL; LOQ, 10, µg/mL; RSD, 1.8% to 11%; and percent recovery, 92% to 96%, while the FTIR method exhibited linear range, 4 to 100 µg/mL; R2, 0.999; LOD, 1.3 µg/mL; LOQ, 3.9 µg/mL; RSD, 0.69% to 10%; and percent recovery, 95% to 96%. The characteristic analytical data indicated that any of the three newly developed methods could be used for the determination of β-carotene in the pumpkin flesh, peel, and seeds. Acetone was used as the extraction solvent for the UV-VIS and NIR spectroscopic methods, and acetonitrile was used as the extraction solvent for the FTIR method. Content of β-carotene was found higher in pumpkin peel (340–445 µg/g), followed by pumpkin flesh (317–341 µg/g) and pumpkin seed (12–17 µg/g) by the UV-VIS method. β-Carotene content was obtained ((376–451 µg/g) and (289–313 µg/g); (210–287 µg/g) and (102–148 µg/g)) using NIR and FTIR methods in pumpkin peel and flesh, respectively. β-Carotene was obtained higher from pumpkin peel by all the three methods than from pumpkin flesh and seed. The β-carotene content in the pumpkin seed was not detected by NIR and FTIR spectroscopic methods.

Silage maturity of maize in a foothill area with NIRS method support

The aim of this work was to identify and quantify the relationships between the classification of maize (Zea mays L.) hybrids into FAO groups and the dry matter content in the dynamics of vegetation development in a foothill area. The work summarises the results of 9 experiments (140 hybrids, 20 FAO groups). Determining dry matter content was made by a combination of gravimetric determination and NIRS method. Dynamics of the development of dry matter content were evaluated at the level of vegetation days and calendar days. The assessment at the level of the calendar day has sufficient informative value and is more suitable for practical use. Climatic factors were expressed by the index of the relative condition of field crops (RKPP) and we monitored their influence on the dynamics of the development of dry matter content. We found statistically significant differences in the RKPP index between the harvest seasons in each area and at one of the three monitored localities. ± 1 unit of RKPP means a shift of ± 2.66 days of silage maturity. We defined the silage maturity in terms of reaching a dry matter content of 30%. In individual experiments, it was reached on average on the 273rd day, but in a wide range of 40 days. This range was 44 days at the level of individual hybrids and the level of FAO groups. The FAO230 group (the most numerous) range of silage maturity reached 28 days. We have compiled a proposal for a model for the evaluation of silage maize hybrids based on silage maturity. The results show that the use of FAO maturity groups does not provide a suitable basis for estimating silage maturity.

Development of calibration models in the spectral range from 1400 to 2500 nm to determine the mass fraction of fat, sugar and moisture in bakery products by the NIR method

Development of calibration models in the spectral range from 1400 to 2500 nm to determine the mass fraction of fat, sugar and moisture in bakery products by the NIR method Scientific journal Vestnik Mezhdunarodnoi Akademii Kholoda