NIR Prediction Research Articles

Accuracy of single and mix calibration as method to determine ruminant feedstuffs nutrient utilization using Near-Infrared Reflectance Spectroscopy

Including real-time data nutrient utilization from quick and accurate methods such as NIRS in a ration formulation improves feed efficiency. However, the homogeneity of the feedstuff sample set used in calibration influences its prediction accuracy and application. This study compares the calibration accuracy of ruminant feed nutrient utilization using a single or mixed calibration. Feedstuffs of different origins (Padang (24), Bandung (24), and Bogor (24)) consisting of forages (30) and concentrate (30) were used separately for single calibration or together for mixed calibration. 12 samples were used for external calibration. The spectrum of each sample was collected using FT-NIRS. The calibration used the PLS regression model. Parallel to the NIRS spectrum collection, nutrient utilization was observed using in vitro methods including degradation of dry matter, organic matter, crude protein, rumen pH, VFA, and NH3 concentration. The results showed accuracy of nutrient utilization prediction varied between the parameters observed. Except for VFA, mixed calibration produced better accuracy (R2>0.83 and RPD>2.21) than single calibration. External calibration proved that NIRS prediction was not significantly different from the in vitro results (P > 0.05). It is concluded mixed ruminant feedstuffs database can be used accurately and broadly to estimate nutrient utilization using NIRS.

NIRS prediction of the protein, fat, and ash of Sargassum fusiforme at different growth stages: A quality control approach

Seaweed is a sustainable source of nutrients for human consumption, and a rapid and accurate quality control approach is essential for ensuring the nutritional value and health benefits of seaweed. The nutritional constituents and quality of seaweed vary significantly along with their growth stages. The present study developed a quality control approach for seaweed Sargassum fusiforme, rapid and convenient, using near-infrared spectroscopy (NIRS) and chemometrics to predict protein, fat, and ash in S. fusiforme at different growth stages. Partial least squares (PLS) regression was utilized to construct the quantitative relationship between NIR spectral data and nutritional components. Moreover, three wavelength selection algorithms, namely, competitive adaptive reweighted sampling (CARS), genetic algorithm, backward interval (BI) and synergy interval, were implemented to optimize the PLS models and improve prediction accuracy. Results demonstrated that the CARS-PLS model exhibited superior performance with a root mean square error of prediction and a coefficient of determination of prediction of 0.1075 % and 0.9936 for protein, 0.1807 % and 0.7706 for fat, and 0.3315 % and 0.9937 for ash, respectively. The effects of different developmental stages on the nutritional quality of S. fusiforme were also investigated. The seedling and early growth stages were the preferable harvest times for preparing high-protein foods and health supplements. The overall results confirmed the strong applicability of NIRS as a rapid and accurate method for measuring the protein, fat, and ash contents of S. fusiforme. This work also recommended that S. fusiforme quality can be controlled in a more precise and refined manner on the basis of a customer-oriented strategy.

Optimised use of data fusion and memory‐based learning with an Austrian soil library for predictions with infrared data

AbstractInfrared spectroscopy in the visible to near‐infrared (vis–NIR) and mid‐infrared (MIR) regions is a well‐established approach for the prediction of soil properties. Different data fusion and training approaches exist, and the optimal procedures are yet undefined and may depend on the heterogeneity present in the set and on the considered scale. The objectives were to test the usefulness of partial least squares regressions (PLSRs) for soil organic carbon (SOC), total carbon (Ct), total nitrogen (Nt) and pH using vis–NIR and MIR spectroscopy for an independent validation after standard calibration (use of a general PLSR model) or using memory‐based learning (MBL) with and without spiking for a national spectral database. Data fusion approaches were simple concatenation of spectra, outer product analysis (OPA) and model averaging. In total, 481 soils from an Austrian forest soil archive were measured in the vis–NIR and MIR regions, and regressions were calculated. Fivefold calibration‐validation approaches were carried out with a region‐related split of spectra to implement independent validations with n ranging from 47 to 99 soils in different folds. MIR predictions were generally superior over vis–NIR predictions. For all properties, optimal predictions were obtained with data fusion, with OPA and spectra concatenation outperforming model averaging. The greatest robustness of performance was found for OPA and MBL with spiking with R2 ≥ 0.77 (N), 0.85 (SOC), 0.86 (pH) and 0.88 (Ct) in the validations of all folds. Overall, the results indicate that the combination of OPA for vis–NIR and MIR spectra with MBL and spiking has a high potential to accurately estimate properties when using large‐scale soil spectral libraries as reference data. However, the reduction of cost‐effectiveness using two spectrometers needs to be weighed against the potential increase in accuracy compared to a single MIR spectroscopy approach.

Prediction of Acoustic Velocity Properties of Downed Pine Trees Using Near-Infrared Spectroscopy

Abstract Near-infrared reflectance (NIR) spectroscopy was used to determine correlations between acoustic velocity and stiffness properties of downed pine trees in the southern coastal plains of the United States. Three acoustic measurement methods (longitudinal, transverse, and offset) were used. From the measurement of the acoustics, the time of flight (TOF) was determined from the downed trees. Increment core samples were obtained from each thirty downed pine trees in the study. NIR spectra were obtained using a fiber probe on the radial surface of each core to rapidly correlate the speed of sound, estimate the strength properties of the downed trees, and the TOF acoustic assessments. The NIR prediction was very good for the transverse and offset methods. The predictability diagnostic was above an R2 of 0.70 for both offset measurements for the transverse methods for the acoustic velocity and dynamic modulus of elasticity (MOE). The longitudinal measurement exhibited the weakest model (R2 < 0.65) for both the acoustic velocity and the MOE with the highest standard error of prediction between 3.0 (ELVLSWV) and 0.31 (VLSWV) for the three measurement types. All the standard errors of calibration were below 1% except in ELVOSWV, which was ∼2%. The dry density measured from the increment cores had a moderate correlation (R2 ∼ 60%), compared with the lower correlation (R2 ∼ 50%) by the green density in the multiple linear regression output. The results of the acoustic model indicated that NIR spectroscopy has the potential to predict the acoustic velocity and corresponding stiffness of downed trees.

In-Line Near-Infrared Spectroscopy Gives Rapid and Precise Assessment of Product Quality and Reveals Unknown Sources of Variation—A Case Study from Commercial Cheese Production

Quality testing in the food industry is usually performed by manual sampling and at/off-line laboratory analysis, which is labor intensive, time consuming, and may suffer from sampling bias. For many quality attributes such as fat, water and protein, in-line near-infrared spectroscopy (NIRS) is a viable alternative to grab sampling. The aim of this paper is to document some of the benefits of in-line measurements at the industrial scale, including higher precision of batch estimates and improved process understanding. Specifically, we show how the decomposition of continuous measurements in the frequency domain, using power spectral density (PSD), may give a useful view of the process and serve as a diagnostic tool. The results are based on a case regarding the large-scale production of Gouda-type cheese, where in-line NIRS was implemented to replace traditional laboratory measurements. In conclusion, the PSD of in-line NIR predictions revealed unknown sources of variation in the process that could not have been discovered using grab sampling. PSD also gave the dairy more reliable data on key quality attributes, and laid the foundation for future improvements.

Developing an NIRS Prediction Model for Oil, Protein, Amino Acids and Fatty Acids in Amaranth and Buckwheat

Amaranth and buckwheat are two pseudo-cereals preferred for their high nutritional value, are gluten free and carry religious importance as fasting food. Germplasm resources are the reservoir of diversity for different traits, including nutritional characteristics. These resources must be evaluated to utilize their potential in crop improvement programs. However, conventional methods are labor-, cost- and time-intensive and prone to handling errors when applied to large samples. NIRS-based machine learning to predict different nutritional traits is applied in different food crops for multiple traits. NIRS prediction models are developed in this study using the mPLS regression technique for oil, protein, fatty acids and essential amino acid estimation in amaranth and buckwheat. Good RSQ external (power of determination) values were obtained for the above traits ranging from 0.72 to 0.929. Ratio performance deviation (RPD) value for most of the traits ranged between 2 and 3, except for valine (1.88) and methionine (3.55), indicating good prediction capabilities in the developed model. These prediction models were utilized in screening the germplasm of amaranth and buckwheat; the results obtained were in good agreement and confirmed the applicability of developed models. It will enable the identification of a trait-specific germplasm as a potential gene source and aid in crop improvement programs.

Prehospital Respiratory Early Warning Score for airway management in-ambulance: A score comparison.

Prehospital Respiratory Early Warning Scores to estimate the requirement for advanced respiratory support is needed. To develop a prehospital Respiratory Early Warning Score to estimate the requirement for advanced respiratory support. Multicentre, prospective, emergency medical services (EMS)-delivered, longitudinal cohort derivationvalidation study carried out in 59 ambulances and five hospitals across five Spanish provinces. Adults with acute diseases evaluated, supported and discharged to the Emergency Department with high priority were eligible. The primary outcome was the need for invasive or non-invasive respiratory support (NIRS or IRS) in the prehospital scope at the first contact with the patient. The measures included the following: epidemiological endpoints, prehospital vital signs (respiratory rate, pulse oximetry saturation, fraction of inspired oxygen, systolic and diastolic mean blood pressure, heart rate, tympanic temperature and consciousness level by the GCS). Between 26 Oct 2018 and 26 Oct 2021, we enrolled 5793 cases. For NIRS prediction, the final model of the logistic regression included respiratory rate and pulse oximetry saturation/fraction of inspired oxygen ratio. For the IRS case, the motor response from the Glasgow Coma Scale was also included. The REWS showed an AUC of 0.938 (95% CI: 0.918-0.958), a calibration-in-large of 0.026 and a higher net benefit as compared with the other scores. Our results showed that REWS is a remarkably aid for the decision-making process in the management of advanced respiratory support in prehospital care. Including this score in the prehospital scenario could improve patients' care and optimise the resources' management.

Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm.

Cowpea (Vigna unguiculata (L.) Walp.) is one such legume that can facilitate achieving sustainable nutrition and climate change goals. Assessing nutritional traits conventionally can be laborious and time-consuming. NIRS is a technique used to rapidly determine biochemical parameters for large germplasm. NIRS prediction models were developed to assess protein, starch, TDF, phenols, and phytic acid based on MPLS regression. Higher RSQexternal values such as 0.903, 0.997, 0.901, 0.706, and 0.955 were obtained for protein, starch, TDF, phenols, and phytic acid respectively. Models for all the traits displayed RPD values of >2.5 except phenols and low SEP indicating the excellent prediction of models. For all the traits worked, p-value ≥ 0.05 implied the accuracy and reliability score >0.8 (except phenol) ensured the applicability of the models. These prediction models will facilitate high throughput screening of large cowpea germplasm in a non-destructive way and the selection of desirable chemotypes in any genetic background with huge application in cowpea crop improvement programs across the world.

NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals.

Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R2 ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade.

Water repellency prediction in high‐organic Greenlandic soils: Comparing vis–NIRS to pedotransfer functions

AbstractSoil water repellency (SWR) is a common phenomenon across agricultural soils of South Greenland that can negatively affect soil functions. Existing methods to measure SWR as a function of water content (w) are laborious. This study was conducted to compare the potential of visible–near‐infrared spectroscopy (vis–NIRS) as an alternative method to pedotransfer functions (PTFs) for predicting four SWR indices in 143 agricultural soils from South Greenland (clay, 0.016–0.172 kg kg–1; organic carbon (OC), 0.009–0.241 kg kg–1). Pedotransfer functions were established by multiple linear regression based on OC, clay, and pH. Partial least squares regression (PLS‐R) and interval PLS‐R were applied to build vis–NIRS prediction models (vis–NIR range 400–2,500 nm). The area under the SWR–w curve (SWRarea) and the critical soil water content (wnon) were accurately predicted by PTFs (R2 = .90; R2adj = .91) while the SWR after 60 °C pretreatment (SWR60), and the integrative repellency dynamic index (IRDI) were predicted less accurately (R2adj = .36; R2adj = .27). Vis‐NIRS models with variable selection performed at a better or close to the same level of accuracy as PTFs (SWRarea, R2 = .88; wnon, R2 = .90; SWR60, R2 = .63; IRDI, R2 = .54). This study demonstrated vis–NIRS as a valuable alternative to PTFs for rapid assessment of SWR and as a tool for SWR mitigation for farmers in South Greenland. The results may well apply to other regions with similar texture and OC ranges, but further testing is required.

Fast Determination of the Rubber Content in Taraxacum kok-saghyz Fresh Biomass Using Portable Near-Infrared Spectroscopy and Pyrolysis–Gas Chromatography

Taraxacum kok-saghyz (TKS) is a plant native to the Tianshan valley on the border between China and Kazakhstan. TKS rubber is a good substitute for natural rubber. TKS's breeding work necessitates the need to screen high-yielding varieties, hence rapid determination of rubber content is essential for the screening. Conventional analytical methods cannot meet actual needs in terms of real-time testing and economic cost. Near-infrared spectroscopy analysis technology, which has developed rapidly in the field of industrial process analysis in recent years, is a green detection technology with obvious merits of fast measurement speed, low cost and no sample loss. This research aims to develop a portable non-destructive near-infrared spectroscopic detection scheme to evaluate the content of natural rubber in TKS fresh roots. Pyrolysis gas chromatography (Py-GC) was chosen as the reference method for the development of NIR prediction model. 208 TKS fresh root samples were collected from the Inner Mongolia Autonomous Region of China. Near-infrared spectra were acquired for all samples. Randomly, two-thirds of them were selected as the calibration set, the remaining one-third as the validation set, and the partial least squares method was successfully established a good NIR prediction model for rubber content at 1080–1800 nm with a ratio of performance to deviation (RPD) of 5.54 and coefficient of determination (R2) of 0.95. This study showed that portable near-infrared spectroscopy could be used with ease for large-scale screening of TKS plants in farmland, and could greatly facilitate TKS germplasm preservation, high-yield cultivation, environment-friendly, high-efficiency and low-cost rubber extraction, and comprehensive advancement of the natural rubber industry thereof.

Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling.

Accuracy of infrared (IR) models to measure soil particle-size distribution (PSD) depends on soil preparation, methodology (sedimentation, laser), settling times and relevant soil features. Compositional soil data may require log ratio (ilr) transformation to avoid numerical biases. Machine learning can relate numerous independent variables that may impact on NIR spectra to assess particle-size distribution. Our objective was to reach high IRS prediction accuracy across a large range of PSD methods and soil properties. A total of 1298 soil samples from eastern Canada were IR-scanned. Spectra were processed by Stochastic Gradient Boosting (SGB) to predict sand, silt, clay and carbon. Slope and intercept of the log-log relationships between settling time and suspension density function (SDF) (R2 = 0.84–0.92) performed similarly to NIR spectra using either ilr-transformed (R2 = 0.81–0.93) or raw percentages (R2 = 0.76–0.94). Settling times of 0.67-min and 2-h were the most accurate for NIR predictions (R2 = 0.49–0.79). The NIR prediction of sand sieving method (R2 = 0.66) was more accurate than sedimentation method(R2 = 0.53). The NIR 2X gain was less accurate (R2 = 0.69–0.92) than 4X (R2 = 0.87–0.95). The MIR (R2 = 0.45–0.80) performed better than NIR (R2 = 0.40–0.71) spectra. Adding soil carbon, reconstituted bulk density, pH, red-green-blue color, oxalate and Mehlich3 extracts returned R2 value of 0.86–0.91 for texture prediction. In addition to slope and intercept of the SDF, 4X gain, method and pre-treatment classes, soil carbon and color appeared to be promising features for routine SGB-processed NIR particle-size analysis. Machine learning methods support cost-effective soil texture NIR analysis.

PREDICTION FOR TOTAL MOISTURE CONTENT IN WOOD PELLETS BY NEAR INFRARED SPECTROSCOPY (NIRS)

Mankind consumes as many fossil fuels per year as nature produced during a million years. Furthermore, energy consumption has tripled in the last 60 years, and is expected to be doubled in 2050, due to the technological and economic takeoff of large emerging powers. In light of this scenario, a change in energy policies should be aimed to promote energy efficiency and the use of sustainable energy resources, such as bioenergy, safely and environmental friendly. Biomass fuels take a main role in the EU's energy and climate policies, aimed at reducing CO2 emissions and combating Climate Change. Quality control of biofuels and knowledge of their characteristics reach a large relevance in any industrial application based on the energy use of biofuels. NIRS technology (near infrared spectroscopy, Near Infrared Reflectance Spectroscopy) is based on the interaction between electromagnetic radiation and material, and now is presented as an encouraging technological tool for quality testing of solid biofuels, since it is a non-destructive and much faster and cheaper analytical technique than traditional ones, which are based on current ISO standards. That interaction appears as spectral vibrations (energy absorption or absorbance) that are properly processed, allowing to measure the most important physical-chemical properties of solid biofuels in a fast, simple and non-destructive way. Wood pellet is the highest quality solid biofuel in the current biomass market, which is regulated by ENplus® certification scheme in the European-wide, ensuring the quality of product to consumers. Low heating value is the most important parameter of solid biofuel, and is strongly linked to total moisture content, so the control of moisture is key factor during pelletizing process. The original research carried out in this work allows to present the development of a NIRS prediction model for total moisture in wood pellets, in the range of values of 5 – 10 %, providing a much faster and more automated methodology for product quality control, both in production plants of wood pellets and in power plants with wood pellets as fuel. Keywords: near infrared spectroscopy, wood pellets, quality control, moisture, prediction model

Cross-laminated timber design by flattened bamboo based on near-infrared spectroscopy and finite element analysis

Bamboo-wood composite cross-laminated timber (BCLT) is a new kind of wood structure material. Studies of the mechanical properties of BCLT have the potential to improve its utilization. Compared with the traditional testing method, this paper designs a fast and effective nondestructive testing method. Three types of composite BCLT plates were designed and made. Multi-point sampling of unit-converted timber was done using a 900-1700 nm NIR spectrometer. Mechanical properties of the unit converted timber were obtained through a four-point bending experiment. The data set consisted of near-infrared spectrum data and mechanical property data. The NIR prediction model was obtained by partial least squares method. The coefficients of determination for the density, MOR and MOE prediction models were 0.88, 0.88, and 0.85, respectively. Finally, the finite element modeling analysis of BCLT plate was carried out according to the element material prediction model, and the prediction of mechanical properties of BCLT plate was achieved. For the three BCLT plates, the prediction error of the finite element model was less than 10%, showing that the finite element analysis method is feasible to predict the mechanical properties of BCLT plate.

Prediction of meat quality traits in the abattoir using portable near-infrared spectrometers: heritability of predicted traits and genetic correlations with laboratory-measured traits

BackgroundThe possibility of assessing meat quality traits over the meat chain is strongly limited, especially in the context of selective breeding which requires a large number of phenotypes. The main objective of this study was to investigate the suitability of portable infrared spectrometers for phenotyping beef cattle aiming to genetically improving the quality of their meat. Meat quality traits (pH, color, water holding capacity, tenderness) were appraised on rib eye muscle samples of 1,327 Piemontese young bulls using traditional (i.e., reference/gold standard) laboratory analyses; the same traits were also predicted from spectra acquired at the abattoir on the intact muscle surface of the same animals 1 d after slaughtering. Genetic parameters were estimated for both laboratory measures of meat quality traits and their spectra-based predictions.ResultsThe prediction performances of the calibration equations, assessed through external validation, were satisfactory for color traits (R2 from 0.52 to 0.80), low for pH and purge losses (R2 around 0.30), and very poor for cooking losses and tenderness (R2 below 0.20). Except for lightness and purge losses, the heritability estimates of most of the predicted traits were lower than those of the measured traits while the genetic correlations between measured and predicted traits were high (average value 0.81).ConclusionsResults showed that NIRS predictions of color traits, pH, and purge losses could be used as indicator traits for the indirect genetic selection of the reference quality phenotypes. Results for cooking losses were less effective, while the NIR predictions of tenderness were affected by a relatively high uncertainty of estimate. Overall, genetic selection of some meat quality traits, whose direct phenotyping is difficult, can benefit of the application of infrared spectrometers technology.

A very boring 120 h: 15 million tablets under a continuous state of control

The aim of this study is to show the robustness of a commercial direct compression system running continuously under relevant process conditions for over 120 h. The study will apply a representative commercial control strategy including real time monitoring of process parameters, rejection of OOS material, use of in-line NIR prediction for blend potency monitoring, application of mathematical RTD modelling for real-time prediction of product potency, and tablet concentration prediction by tablet NIR.The study showed that the process can produce in specification product robustly over significant operating times. Across 122 h of actual run time (including pauses) the process manufactured over 15 million tablets to give a product yield of 99.2%. Whilst the purpose of this study was to evaluate the process for over 120 h, this process is not limited to running longer periods of time given the performance of the equipment and the control strategy.

Location, year, and tree age impact NIR-based postharvest prediction of dry matter concentration for 58 apple accessions

Abstract Conventional assessments of postharvest traits, like dry matter concentration (DMC) require destructive sampling that can often be limited by fruit availability of apple trees in early years. Alternatively, nondestructive prediction of postharvest traits using NIR spectroscopy has been demonstrated to be rapid and robust in various fruits and vegetables, including apples. With most prediction models built using one to few cultivars, it remains largely unknown if NIR-based prediction can be applied in an apple breeding program with numerous selections that are added, replicated, and evaluated annually. In this study, 2252 fruit from 58 accessions grown at three orchard sites were destructively measured for DMC, and nondestructively measured for NIR spectra. Nondestructive prediction of DMC exhibited high accuracies in most analyses. In characterizing DMC predictive performance of within- versus between-years, both models were highly predictive and comparable, albeit slightly higher for the former. Further analysis of location × year effects revealed that location was a more important factor than year in influencing predictive performance. Finally, in cultivar-specific models, prediction made using fruit from more established trees as a calibration set consistently yielded higher prediction accuracy. Overall, the use of three statistical metrics enhanced our understanding of NIR prediction accuracy, while a control calibration set in all analyses provided a statistical baseline for comparing accuracy outputs. This study provides a framework for understanding the impacts of location, year and tree age on NIR prediction accuracy of DMC in diverse apple breeding accessions. In addition, this study demonstrates the importance of assessing predictive performance using multiple statistical metrics.

NIRS prediction of dry matter content of single olive fruit with consideration of variable sorting for normalisation pre-treatment

Abstract The assessment of dry matter content (DMC) as a surrogate for oil content of single olive fruit was considered in terms of spatial variation in DMC, optimisation of spectral pre-treatments, wavelength range and calibration transfer procedure. There was no consistent variation in DMC from apical to distal end of the fruit. Short wave near infrared spectra (to 1100 nm) were acquired of single fruit using an interactance geometry, with the small size of the fruit resulting in baseline variation between samples. The pre-treatment methods of first derivative (D1), second derivative (D2), standard normal variate transformation (SNV), normalized spectral ratio (NSR), variable sorting for normalization (VSN) and their combinations were applied prior to development of partial least squares regression models on DMC. Root mean square error of prediction (RMSEP) values were reduced with use of the SNV-VSN-D2 pre-treatment (by 34 and 35 %) to 0.95 and 0.88 %, for two handheld NIRS instruments, used in prediction of an external set, n = 212. The percentage of the predicted population within the limits of ± 5 % of actual values was 92.5 and 88.7 % for the VSN PLSR models and 83.0 and 77.3 % for the original models, for two instruments, respectively. Variable selection allowed a further improvement in RMSEP values (to 0.93 and 0.87 %) for the two instruments, respectively. For transfer of models between instruments, model updating based on SNV-VSN-D2 pre-treated spectra resulted in a RMSEP of 1.00 %, compared to 1.79 % for D2 pre-treated spectra, outperforming the classical calibration transfer methods of piecewise direct standardization (PDS) and spectral space transformation (SST). The results have significance to the practical implementation of NIRS-DMC estimation of olive fruit in field conditions.

Prediction of drug dissolution from Toremifene 80 mg tablets by NIR spectroscopy

The aim of our study was to justify substitution of dissolution analysis for NIR measurement of Toremifene 80mg tablets. We studied implementation of a NIRS method by integrating the method development to discrimination power of the dissolution method. Hence, we analyzed 20 DoE tablet batches and studied which of the critical formulation factors affecting dissolution were statistically significant. To study if these factors can be detected by NIRS, PLS calibration models were developed. Finally, PLS model was built to correlate NIR data with the actual dissolution results to predict the released amount of toremifene in 30min. To obtain the data the tablet batches were measured by NIR using diffuse reflectance technique and multivariate analysis tool was used to calibrate the NIRS models. Correlations between the critical formulation factors and the NIR spectra of Toremifene 80mg tablet were shown and it was thus justified to develop a NIRS prediction model for dissolution. Variance (R2), standard error of estimate (SEE) and standard error of prediction (SEP) of the model were 90.0%, 4.3% and 5.9%, respectively. It was thus shown that multi-phased and time consuming dissolution procedure could be substituted for fast non-invasive NIRS method.

Desarrollo de Modelos NIRS de Predicción para el Análisis de la Finura de Fibras Textiles de Vicuña y Llama

La producción de fibras textiles de llama y vicuña es una de las actividades de mayor valor ambiental, cultural y económico para la región alto-andina de Catamarca. Por esto es necesario desarrollar una tecnología que permita analizar de manera rápida y sustentable los parámetros de calidad de este material. En este trabajo se evaluaron, a través de los estadísticos R2, SECV, SEV y RPD, distintos tratamientos y modelos NIRS de predicción para determinar la Finura de muestras de llama y vicuña. Los resultados mostraron que los mejores modelos se obtuvieron con ninguno o con el mínimo tratamiento. Los valores de R2<0.7 y RPD≅1.7 obtenidos, dieron cuenta de la necesidad de aumentar el tamaño muestral y de explorar en el desarrollo de técnicas alternativas de regresión.