Near-infrared Spectrophotometers Research Articles

Mineral and particle-size chemometric classification using handheld near-infrared instruments for soil in Northeast Brazil

The characterization of soil variations crucial for agriculture is challenging due to soil having different mineral composition and particle-size distribution. Traditional methods are costly and time-consuming for large-sized areas. Spectroscopic techniques coupled with chemometrics are alternative ways to overcome these drawbacks. Miniaturized near-infrared (NIR) spectrophotometers provide fast, cost-effective spectra acquisition for assessing soil chemistry and distribution despite challenges like overlapped bands and reduced spectral range. This study presents a pattern recognition strategy to address these limitations, enhancing the use of handheld NIR instruments for soil analysis. The study analyzed 176 soil samples from 15 soil groups in the Northeast region of Brazil. First, attenuated total reflectance-Fourier transform infrared (ATR-FTIR) and energy-dispersive X-ray fluorescence spectrometry (EDXRF) were employed to characterize the samples, providing complementary vibrational and elemental information, respectively. Common Dimension Analysis (ComDim) identified links between ATR-FTIR and EDXRF data, aiding soil characterization. The Common Components (CCs) from ComDim were used in Partitioning Around Medoids (PAM) clustering, resulting in five distinct classes based on their mineral composition. These classes showed significant differences in clay and sand contents. With the use of ComDim-PAM, samples were labeled for classification via Partial-least Squares-Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM) using NIR spectra (spectral range: 908–1676 nm) from two handheld instruments (Hand 1 and Hand 2). The SVM models outperformed the PLS-DA models, especially by including variable selection for Hand 1, with test accuracy exceeding 90%. These findings highlight the method's advantages for fast and cost-effective assessment, classification, and soil mapping based on their mineral and particle-size distribution.

Understanding changes of laser backscattering imaging parameters through the kiwifruit softening process using time series analysis

ABSTRACT During kiwifruit storage, quality monitoring is required for inventory planning and consistent quality maintenance. Commercial near-infrared (NIR) spectrophotometers showed reduced performance in the estimation of kiwifruit flesh firmness (FF) as the FF estimation is indirect and can be affected when both, textural structures and absorbing compounds, change during postharvest ripening. Laser backscattering imaging (LBI) records the backscattered signal after a single laser beam interacts with kiwifruit tissue, including merged information on light absorption and scattering. Measurements were carried out at 830 nm, where scattering is most dominant. In this work, time series of kiwifruit ‘Zesy002’ (n = 30) and ‘Hayward’ (n = 30) LBI were collected through the postharvest ripening during a 15-day shelf life at 20°C. Four LBI parameters capturing DIP, FWHM, SLP and HWQM were selected in this study. ‘‘Zesy002’ DIP, FWHM, SLP, and HWQM increased approx. 0.6 cm, 0.2 cm, 0.3 and 0.14 cm, respectively. ‘Hayward’ LBI increased approx. 0.2 cm, 0.1 cm, 0.2 and 0.04 cm, respectively. Different initial LBI values between cultivars and LBI changes may reflect the actual stage of softening, affected by kiwifruit ripeness. In conclusion, time series analysis could be useful in describing kiwifruit LBI change during ripening and making forecasts.

Field assessment of active ingredient quantity in pharmaceutical tablets with limited calibration of near infrared spectra: An application to ciprofloxacin tablets

Portable near-infrared (NIR) spectrophotometers have emerged as valuable tools for identifying substandard and falsified pharmaceuticals (SFPs). Integration of these devices with chemometric and machine learning models enhances their ability to provide quantitative chemical insights. However, different NIR spectrophotometer models vary in resolution, sensitivity, and responses to environmental factors such as temperature and humidity, necessitating instrument-specific libraries that hinder the wider adoption of NIR technology. This study addresses these challenges and seeks to establish a robust approach to promote the use of NIR technology in post-market pharmaceutical analysis. We developed support vector machine and partial least squares regression models based on binary mixtures of lab-made ciprofloxacin and microcrystalline cellulose, then applied the models to ciprofloxacin dosage forms that were assayed with high performance liquid chromatography (HPLC). A receiver operating characteristic (ROC) analysis was performed to set spectrophotometer independent NIR metrics to evaluate ciprofloxacin dosage forms as “meets standard,” “needs HPLC assay,” or “fails standard.” Over 200 ciprofloxacin tablets representing 50 different brands were evaluated using spectra acquired from three types of NIR spectrophotometer with 85% of the prediction agreeing with HPLC testing. This study shows that non-brand-specific predictive models can be applied across multiple spectrophotometers for rapid screening of the conformity of pharmaceutical active ingredients to regulatory standard.

Development of novel portable NIR spectroscopy process analytical technology (PAT) tool for monitoring the transition of ibuprofen to ibuprofen sodium during wet granulation process

The aim of this research was to develop a process analytical technology (PAT) tool for monitoring the transformation of the active ingredient ibuprofen into the fast-dissolving salt ibuprofen sodium during the wet granulation process. Two near-infrared (NIR) spectrophotometers, portable and benchtop spectrophotometer, were compared. During the analysis with the built models, both demonstrated comparable accuracy and precision (R2X = 0.995, R2Y = 0.927, Q2 = 0.995, and R2X = 0.990, R2Y = 0.948, Q2 = 0.992, respectively). Considering the applicability, a model based on the portable NIR spectroscopic data was chosen for further development and application as a PAT tool for monitoring different steps during the wet granulation process. The evaluation of the model's predictive capability involved analyzing laboratory trial batches with varying amounts of sodium carbonate, resulting in different concentrations of ibuprofen sodium at the end of the wet granulation process. Subsequently, tablets were manufactured from each trial batch, followed by dissolution analysis. The dissolution rate assays were in good agreement with the NIR-predicted concentrations of ibuprofen sodium at the end of the wet granulation process. Based on the results, the proposed model provides an excellent tool to monitor the ibuprofen acid-salt transformation, to determine the end-point of the reaction, and to efficiently control the wet granulation process.

Application of a short-wave pocket-sized near-infrared spectrophotometer to predict milk quality traits

Portable-hand-held devices based on near-infrared (NIR) technology have improved and are gaining popularity, even if their implementation in milk has been barely evaluated. Thus, the aim of the present study was to assess short-wave pocket-sized NIR devices' feasibility to predict milk quality. A total of 331 individual milk samples from different cow breeds and herds were collected in 2 consecutive days for chemical determination and spectral collection by using 2 pocket-sized NIR spectrophotometers working in the range of 740-1070 nm. The reference data was matched with the corresponding spectrum and modified-partial least-squares regression models were developed. A 5-fold cross-validation was applied to evaluate individual devices' performance and an external-validation with 25% of the data set as the validation set was applied for the final models. Results revealed that both devices' absorbance was highly correlated but greater for instrument A than B. Thus, the final models were built by averaging the spectra from both devices for each sample. The fat content prediction model was adequate for quality control with a coefficient of determination (R2ExV) and a residual predictive deviation (RPDExV) in external validation of 0.93 and 3.73, respectively. Protein and casein content as well as fat-to-protein ratio prediction models might be used for a rough screening (R2ExV > 0.70; RPDExV > 1.73). However, poor prediction models were obtained for all the other traits with an R2ExV between 0.43 (urea) and 0.03 (somatic cell count), and a RPDExV between 1.18 (urea) and 0.22 (somatic cell count). In conclusion, short-wave portable-hand-held NIR devices accurately predicted milk fat content, and protein, casein, and fat-to-protein ratio might be applied for rough screening. It seems that there is not enough information in this NIR region to develop adequate prediction models for lactose, somatic cell count, urea, and freezing point.

In-Field Implementation of Near-Infrared Quantitative Methods for Analysis of Medicines in Tropical Environments.

Near-infrared (NIR) spectroscopy is actually a well-established technique that demonstrates its performance in the frame of detection of poor-quality medicines. The use of low-cost handheld NIR spectrophotometers in low-resource contexts can allow an inexpensive and more rapid detection compared to laboratory methods. Considering these points, it was decided to develop, validate, and transfer methods for the quantification of ciprofloxacin and metronidazole tablet samples using a NIR handheld spectrophotometer in transmission mode (NIR-M-T1) coupled to chemometrics such as partial least squares regression (PLSR) algorithm. All of the models were validated with the total error approach using an accuracy profile as a decision tool, with ±10% specifications and a risk α set at 5%. Quantitative PLSR models were first validated in Belgium, which is a temperate oceanic climate zone. Second, they were transferred to Cameroon, a tropical climate zone, where issues regarding the prediction of new validation series with the initial models were highlighted. Two augmentation strategies were then envisaged to make the predictive models robust to environmental conditions, incorporating the potential variability linked to environmental effects in the initial calibration sets. The resulting models were then used for in-field analysis of ciprofloxacin and metronidazole tablet samples collected in three cities in Cameroon. The contents results obtained for each sample with the two strategies were close and not statistically different. Nevertheless, the first one is easier to implement and the second is the best regarding model diagnostic measures and accuracy profiles. Two samples were found to be noncompliant in terms of content, and these results were confirmed using high-performance liquid chromatography taken as the reference method.

Large-scale measurement of soil organic carbon using compact near-infrared spectrophotometers: effect of soil sample preparation and the use of local modelling

Compact NIR instruments are a cost-effective alternative for large-scale SOC determination. The best performance was found using local modelling and soil sample preparation.

Feasibility of compact near-infrared spectrophotometers and multivariate data analysis to assess roasted ground coffee traits

Two low-cost, miniaturized near-infrared (NIR) spectrophotometers working in two different spectral ranges (900–1700 nm and 1300–2500 nm) and employing different spectrometric technologies were evaluated to assess several traits of ground roasted commercial coffees, using multivariate analysis. The comprehensive sample set comprised one hundred forty-five samples from different origins, blends of varietals (arabica/arabica, and arabica/robusta) or pure varietal (arabica), decaffeinated or regular, presenting several grades of sensorial traits (bitterness, intensity, acidity, body, and roast degree), as informed by the manufacturing company. Representative spectra of the samples were acquired using a probing device providing rotational and linear displacement of the sample vial. The spectral data set was evaluated using principal component analysis (PCA) models aiming to verify the discrimination capability of both instruments. When evaluated down to the fourth principal component by both compact instruments, origin, varietal, decaffeinated, and regular coffees are grouped and well-differentiated by the PCA scores. Differences among batches of the same coffee brand can be observed. Quantitative models based on partial least square (PLS) regression were evaluated to assess the feasibility of miniaturized spectrophotometers to determine the samples' intensity, bitterness, body, roast, and acidity traits. These sensorial traits can be predicted by partial least square models with root mean square errors of external validation of 1.3, 0.7, 0.6, 0.5, and 0.8 and 1.2, 0.7, 0.5, 0.5, and 0.7 for intensity, bitterness, body, roast, and acidity, respectively, using the data sets obtained by the NanoNIR, and the NeoSpectra instruments and employing variables selected by the Jack-Knife algorithm. Quantitative results can be improved using local regression models constructed with a restricted set of samples. Overall, the results reveal the potential of miniaturized, low-cost NIR spectrophotometers to characterize commercial ground roasted coffee qualitatively and quantitatively with a slight advantage of using the instrument providing the more comprehensive wavelength range and based on Fourier transform technology.

Indicator Minerals, Pathfinder Elements, and Portable Analytical Instruments in Mineral Exploration Studies

Until recently, the classic approach to mineral exploration studies was to bring the field samples/drill cores collected during field studies to the laboratory, followed by laborious analysis procedures to generate the analytical data. This is very expensive, time-consuming, and difficult for exploring vast areas. However, rapid technological advances in field-portable analytical instruments, such as portable visible and near-infrared spectrophotometers, gamma-ray spectrometer, pXRF, pXRD, pLIBS, and µRaman spectrometer, have changed this scenario completely and increased their on-site applications in mineral exploration studies. LED fluorimeter is a potential portable tool in the hydrogeochemical prospecting studies of uranium. These instruments are currently providing direct, rapid, on-site, real-time, non-destructive, cost-effective identification, and determination of target elements, indicator minerals and pathfinder elements in rock, ore, soil, sediment, and water samples. These portable analytical instruments are currently helping to obtain accurate chemical and mineralogical information directly in the field with minimal or no sample preparation and providing decision-making support during fieldwork, as well as during drilling operations in several successful mineral exploration programs. In this article, the developments in these portable devices, and their contributions in the platinum group elements (PGE), rare earth elements (REE), gold, base metals, and lithium exploration studies both on land and on the ocean bed, have been summarized with examples.

Effect of the sample measurement representativeness on soil carbon determination using near-infrared compact spectrophotometers

Compact or miniaturized near-infrared (NIR) spectrophotometers are inexpensive instruments that can boost the use of NIR spectroscopy (NIRS) for determining soil organic carbon (SOC). Nevertheless, these instruments are deficient concerning the probing capability due the small area of the sample assessed in spectra measurement. Protocols normally used during spectral data acquisition can be insufficient to obtain representative spectra, which can affect the performance of the models to estimate SOC. This paper aimed to understand how the representativeness of soil spectra is affected by increasing the area probed during spectra acquisition and determine its effect on the performance of multivariate regression models constructed using compact NIR spectrophotometers for SOC determination. Four measurement modes of the flask containing the soil sample were evaluated using a compact FT-NIR. Measurement modes I (full radial and axial scanning of sample flask) and II (full radial scanning in a fixed axial line of sample flask) were performed using a rotational-linear sample probing device to move the sample flask over the optical probing beam during spectral data acquisition. Measurement modes III and IV, in turn, were performed in static mode. Using the measurement mode III, the spectral data were acquired at variable points of the sample flask, and using the measurement mode IV, the spectral data were acquired at a single fixed point in the sample flask. The study was developed in two stages. Firstly, five spectra of three soil samples (selected according to their carbon content) were acquired using the measurement modes. The repeatability and representativeness of the spectral data were then evaluated using principal component analysis (PCA). Partial least square regression (PLSR) models were constructed to determine the quantitative effects of the representativeness of the four measurement modes used to acquire the spectral data set. The calibration and independent validation sets were composed of 49 and 20 soil samples, respectively. The increase in the sample area probed using the a rotational-linear sample probing device resulted in more representative and reproducible spectral data, which improves the performance of compact spectrophotometer in SOC determination. Significant enhancements in SOC determination ranged from 146 to 400 % were found, considering the most (mode I) over less representative measurement modes. These results were verified by a comparative randomization test between models’ prediction performances. In this context, using a rotational-linear sample probing device is recommended for SOC determinations using compact NIR spectrophotometers.

Tuning Electronic and Morphological Properties for High‐Performance Wavelength‐Selective Organic Near‐Infrared Cavity Photodetectors

AbstractIncorporation of compact spectroscopic near‐infrared (NIR) light detectors into various wearable and handheld devices opens up new applications, such as on‐the‐spot medical diagnostics. To extend beyond the detection window of silicon, i.e., past 1000 nm, organic semiconductors are highly attractive because of their tunable absorption. In particular, organic NIR wavelength‐selective detectors have been realized by incorporating donor:acceptor thin films, exhibiting weak intermolecular charge‐transfer (CT) absorption, into an optical microcavity architecture. In this work, the alkyl side chains of the well‐known PBTTT donor polymer are replaced by alkoxy substituents, hereby redshifting the CT absorption of the polymer:PC61BM blend. It is shown that the unique fullerene intercalation features of the PBTTT polymer are retained when half of the side chains are altered, hereby maximizing the polymer:fullerene interfacial area and thus the CT absorption strength. This is exploited to extend the detection range of organic narrow‐band photodetectors with a full‐width‐at‐half‐maximum of 30–38 nm to wavelengths between 840 and 1340 nm, yielding detectivities in the range of 5 × 1011 to 1.75 × 1010 Jones, despite the low CT state energy of 0.98 eV. The broad wavelength tuning range achieved using a single polymer:fullerene blend renders this system an ideal candidate for miniature NIR spectrophotometers.

A rotational-linear sample probing device to improve the performance of compact near-infrared spectrophotometers

A novel device to probe solid ground samples by simultaneous rotational and linear displacement of their container is described and evaluated. The device improves the representativeness of spectra obtained from laboratory samples measured by near-infrared (NIR) spectroscopy using low-cost, compact instruments. These instruments typically illuminate and probe a small portion of the laboratory sample, affecting the quantitative and qualitative analytical results due to a lack of sample representativeness of the NIR spectrum. The device maximizes the sample amount assessed during the measurement, providing a more representative average spectrum. Principal component analysis of the spectral data was employed to evaluate and guide the minimization of the effect of sample heterogeneity on the analytical results. A significant improvement in the range 70 to 300% in the quantitative multivariate regression results over less representative measurement modes was obtained by employing the new device and the rotational-linear mode.

New perspective for the in-field analysis of cannabis samples using handheld near-infrared spectroscopy: A case study focusing on the determination of Δ9-tetrahydrocannabinol

The aim of the present study was to explore the feasibility of applying near-infrared (NIR) spectroscopy for the quantitative analysis of Δ9-tetrahydrocannabinol (THC) in cannabis products using handheld devices. A preliminary study was conducted on different physical forms (entire, ground and sieved) of cannabis inflorescences in order to evaluate the impact of sample homogeneity on THC content predictions. Since entire cannabis inflorescences represent the most common types of samples found in both the pharmaceutical and illicit markets, they have been considered priority analytical targets. Two handheld NIR spectrophotometers (a low-cost device and a mid-cost device) were used to perform the analyses and their predictive performance was compared. Six partial least square (PLS) models based on reference data obtained by UHPLC-UV were built. The importance of the technical features of the spectrophotometer for quantitative applications was highlighted. The mid-cost system outperformed the low-cost system in terms of predictive performance, especially when analyzing entire cannabis inflorescences. In contrast, for the more homogeneous forms, the results were comparable.The mid-cost system was selected as the best-suited spectrophotometer for this application. The number of cannabis inflorescence samples was augmented with new real samples, and a chemometric model based on machine learning ensemble algorithms was developed to predict the concentration of THC in those samples. Good predictive performance was obtained with a root mean squared error of prediction of 1.75 % (w/w). The Bland-Altman method was then used to compare the NIR predictions to the quantitative results obtained by UHPLC-UV and to evaluate the degree of accordance between the two analytical techniques. Each result fell within the established limits of agreement, demonstrating the feasibility of this chemometric model for analytical purposes.Finally, resin samples were investigated by both NIR devices. Two PLS models were built by using a sample set of 45 samples. When the analytical performances were compared, the mid-cost spectrophotometer significantly outperformed the low-cost device for prediction accuracy and reproducibility.

Detection of Lipid Oxidation in Infant Formulas: Application of Infrared Spectroscopy to Complex Food Systems.

Fish- or algal oils have become a common component of infant formula products for their high docosahexaenoic acid (DHA) content. DHA is widely recognized to contribute to the normal development of the infant, and the European Commission recently regulated the DHA content in infant formulas. For many manufacturers of first-age early life nutrition products, a higher inclusion level of DHA poses various challenges. Long-chain polyunsaturated fatty acids (LC-PUFAs) such as DHA are very prone to oxidation, which can alter the organoleptic property and nutritional value of the final product. Traditional methods for the assessment of oxidation in complex systems require solvent extraction of the included fat, which can involve harmful reagents and may alter the oxidation status of the system. A rapid, efficient, non-toxic real-time method to monitor lipid oxidation in complex systems such as infant formula emulsions would be desirable. In this study, infrared spectroscopy was therefore chosen to monitor iron-induced oxidation in liquid infant formula, with conjugated dienes and headspace volatiles measured with GC-MS as reference methods. Infrared spectra of infant formula were recorded directly in mid- and near-infrared regions using attenuated total reflectance Fourier-transform (ATR-FTIR) and near-infrared (NIRS) spectrophotometers. Overall, good correlation coefficients (R2 > 0.9) were acquired between volatiles content and infrared spectroscopy. Despite the complex composition of infant formula containing proteins and sugars, infrared spectroscopy was still able to detect spectral changes unique to lipid oxidation. By comparison, near-infrared spectroscopy (NIRS) presented better results than ATR-FTIR: prediction error ATR-FTIR 18% > prediction error NIRS 9%. Consequently, NIRS demonstrates great potential to be adopted as an in-line or on-line, non-destructive, and sustainable method for dairy and especially infant formula manufacturers.

Simultaneous detection of quality and safety in spinach plants using a new generation of NIRS sensors

Near infrared (NIR) spectrophotometers require study of the spectral acquisition process, so that they can be used for quality and safety assessment of horticultural products. The aim of this work was to optimize the use of two NIR spectrophotometers for analysing spinach plants in situ and online: a manual, portable instrument based on Linear Variable Filter (LVF) technology (MicroNIR™ 1700), suitable for analysis in the field, and during harvest and storage; and a Fourier Transform (FT)-NIR instrument (Matrix-F) suitable for the online analysis in the sorting lines. 195 spinach plants were used to predict the quality (texture, dry matter and soluble solid contents) and safety (nitrate content) parameters. Using the MicroNIR™ 1700 to take 6 spectra per spinach leaf resulted in NIRS models of predictive capacity which enable to screen spinach plants in situ and decide on their industrial destination according to their nitrate content. For the Matrix-F instrument, a single spectrum taken online for the intact product (either moving or not) on the conveyor belt was sufficient to establish product quality and safety during industrial processing. The results also showed that the use of both instruments could form a complementary strategy for global monitoring, allowing spinach plants to be analysed throughout the food supply chain.

Near-infrared spectroscopy-based methods for quantitative determination of active pharmaceutical ingredient in transdermal gel formulations

Semi-solid pharmaceutical forms, like topical and transdermal products, are widely used for medical purposes since they provide convenient and pain-free treatment for patients. In this study, two transdermal gel formulations with different active pharmaceutical ingredients were analyzed with near-infrared spectrophotometers with three distinct optical arrangements (dispersive, Fourier transform, and diode-array spectrophotometers) for the measurement of active pharmaceutical ingredient concentration. The reference concentrations were measured with high-performance liquid chromatography methods.The developing of calibration for reference concentrations based on near-infrared spectra was performed using partial least squares regression. The spectra from near-infrared spectrophotometer with diode-array optical arrangement presented the best root mean square error of cross-validation results with 0.045% and 0.144% for the two active pharmaceutical ingredients. The root mean square error of cross-validation using dispersive spectra were 0.125% and 0.193% and using Fourier transform spectra were 0.130% and 0.344% for the active pharmaceutical ingredients. The results of partial least squares regression were confirmed using simulated spectra based on the measured datasets.Near-infrared spectroscopy can be used successfully for the quantitative measurement of active pharmaceutical ingredient concentration in transdermal gel formulations. The type of optical arrangements of spectrophotometers can influence the performance of calibrations and have to be optimized case by case.

Measuring of food additives via polyethylene foils by NIR spectrophotometers using different optical arrangements

Near-infrared (NIR) spectrophotometers with different optical arrangements were used to measure the diffusely reflected electromagnetic radiation of different types of food additives in polyethylene (PE) foils. Eight compounds, frequently used in food process – but also in other industries –, were measured by different NIR spectrophotometers. The detected (‘as is’) and mathematically transformed (by scatter correction, second derivative combined with smoothing) NIR spectra were processed with multivariate data analysis (MDA). In this matter, unsupervised methods like principal component analysis (PCA) and cluster analysis (CA) were used, which techniques do not require prior information and reference measurements. The aim of the present study was to distinguish food additives by the help of the applied chemometric methods based on NIR spectra detected via the PE foils. Results indicated that distinction of different food additives and compounds with NIR methods is possible not only with the conventional sample preparations and handlings, but also without breaking the packaging.

Determination of Proteins in Human Serum by Near-Infrared Spectroscopy with Partial Least Square Analysis

ABSTRACTNear-infrared spectroscopy with interval and synergy interval partial least squares were used to determine total proteins, albumin, and globulin in human serum. Quantitative models of the relationships between the near-infrared spectra and these proteins were established. The root mean square error of prediction (RMSEP) was used to evaluate the models. The results demonstrate that synergy interval partial least squares was superior to interval partial least squares. The optimum wavelength ranges using synergy interval partial least squares for total proteins, albumin, and globulin in serum were 1668–1772 and 1772–1874 nm; 812–1000 and 1636–1800 nm; and 1228–1364 and 1642–1772 nm, respectively. The RMSEP of these proteins were 0.6905, 0.6686, and 0.5355 g/L. Waveband optimization was necessary to enhance the accuracy. The synergy interval partial least square model for total proteins, albumin, and globulin in serum offered higher accuracy and overcomes shortcomings of interval partial least squares. Furthermore, the optimized spectrum region may allow the design of near-infrared spectrophotometers for specific applications.

短波長域用および長波長域用の携帯型近赤外分光計によるマダイの背肉およびウマヅラハギの肝臓の脂質測定

短波長域用および長波長域用の携帯型近赤外分光計によるマダイの背肉およびウマヅラハギの肝臓の脂質測定

Measurement of Single Soybean Seed Attributes by Near-Infrared Technologies. A Comparative Study

Four near-infrared spectrophotometers, and their associated spectral collection methods, were tested and compared for measuring three soybean single-seed attributes: weight (g), protein (%), and oil (%). Using partial least-squares (PLS) and four preprocessing methods, the attribute that was significantly most easily predicted was seed weight (RPD > 3 on average) and protein the least. The performance of all instruments differed from each other. Performances for oil and protein predictions were correlated with the instrument sampling system, with the best predictions using spectra taken from more than one seed angle. This was facilitated by the seed spinning or tumbling during spectral collection as opposed to static sampling methods. From the preprocessing methods utilized, no single one gave the best overall performances but weight measurements were often more successful with raw spectra, whereas protein and oil predictions were often enhanced by SNV and SNV + detrending.