NIR Spectrometer Research Articles

Rapid evaluation on pharmacodynamics of Curcumae Rhizoma based on Micro-NIR and benchtop-NIR

It’s far from enough to describe the blood circulation promoting effect of Curcumae Rhizoma (CR), which is widely grown as a functional vegetable or spice in south and southeast Asian countries, and processed Curcumae Rhizoma (PCR), only by disclosing the content of a couple of relative compounds. In this study, the thrombin inhibitory effect as well as 2,2′-azino-bis (3-ethylbenz-thiazoline-6-sulfonic acid) (ABTS) and 2,2-diphenyl-picrylhydrazyl (DPPH) free radical scavenging ability of CR/PCR extracts was investigated, and TANGO Fourier transform near-infrared (FT-NIR, Bruker, Germany)—a benchtop instrument allowing the full range NIR wavelength scanning—and handheld-NIR spectrometer (Micro-NIR, NIR-S-G1, InnoSpectra corporation, China) that can be connected to smart phones were used to realize the rapid detection of pharmacodynamic indicators. model was evaluated based on the determination coefficient (R2), root mean square error (RMSE), standard error of test set (SEP) and ratio of performance to deviation (RPD). The results of pharmacodynamics experiment confirmed for the first time that CR has significant inhibitory effect on thrombin, and the modeling results revealed that Micro-NIR had a good prediction on the antioxidant capacity (ABTS and DPPH free radical clearance) with RPD greater than 3, but showed a general predictive performance on thrombin inhibition ability (RPD = 2.434). In contrast, FT-NIR provided a good prediction for all the three indicators, with R2 greater than 0.9 and RPD greater than 4.5. Further insights into the capability of the two devices were obtained by analyzing the wavebands selection work. In the full wavelength range, wavebands related to thrombin inhibition were mainly distributed in the combination area which is out of the reach of handheld Micro-NIR, thus resulting in a decrease in the prediction ability. Therefore, compared to the benchtop-NIR, the detection range of the handheld-NIR is the main factor limiting its capability Based on an overall assessment, handheld NIR spectrometer, by greatly expanding the application scenario of NIR technology, is considered as a useful device with a satisfying predictive ability through model construction.

Evaluation of the grain quality of soft wheat using a NIR spectrometer

Evaluation of the grain quality of soft wheat using a NIR spectrometer

Vis-NIR and XRF Data Fusion and Feature Selection to Estimate Potentially Toxic Elements in Soil.

Soil contamination by potentially toxic elements (PTEs) is intensifying under increasing industrialization. Thus, the ability to efficiently delineate contaminated sites is crucial. Visible–near infrared (vis–NIR: 350–2500 nm) and X-ray fluorescence (XRF: 0.02–41.08 keV) spectroscopic techniques have attracted tremendous attention for the assessment of PTEs. Recently, the application of fused vis–NIR and XRF spectroscopy, which is based on the complementary effect of data fusion, is also increasing. Moreover, different data manipulation methods, including feature selection approaches, affect the prediction performance. This study investigated the feasibility of using single and fused vis–NIR and XRF spectra while exploring feature selection algorithms for the assessment of key soil PTEs. The soil samples were collected from one of the most heavily polluted areas of the Czech Republic and scanned using laboratory vis–NIR and XRF spectrometers. Univariate filter (UF) and genetic algorithm (GA) were used to select the bands of greater importance for the PTE prediction. Support vector machine (SVM) was then used to train the models using the full-range and feature-selected spectra of single sensors and their fusion. It was found that XRF spectra alone (primarily GA-selected) performed better than single vis–NIR and fused spectral data for predictions of PTEs. Moreover, the prediction models that were derived from the fused data set (particularly the GA-selected) enhanced the models’ accuracies as compared with the single vis–NIR spectra. In general, the results suggest that the GA-selected spectra obtained from the single XRF spectrometer (for As and Pb) and from the fusion of vis–NIR and XRF (for Pb) are promising for accurate quantitative estimation detection of the mentioned PTEs.

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.

Nondestructive prediction model of internal hardness attribute of fig fruit using NIR spectroscopy and RF

Hardness is one of the most important quality characteristics, which has an important influence on the processing and product quality of figs. A rapid non-destructive detection method for the hardness of figs was proposed based on visible/near infrared (VIS/NIR) spectroscopy technology. This study attempts to optimize the construction of a fig hardness model and predict the accuracy of thereof. An NIR spectrometer was used to collect the diffuse reflectance spectrum data in the wavelength range of 950–1700 nm, while the hardness index was measured using texture analyzer. Random forest (RF) and partial least square (PLS) methods were used to model the spectral data and hardness, respectively, and a better algorithm for the model construction was obtained. The RF model performed better in the characteristic band (1150.83–1232.43 nm), with correlation coefficient (R2), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP) of 0.76, 67.61, and 83.94 respectively. The PLS model worked well at the full band (R2 = 0.77, RMSEC = 59.20, RMSEP = 91.84). However, the prediction time of the PLS was slightly shorter than that of RF model (0.0004 s < 0.0098 s). The results show that it is feasible to detect the hardness of figs without destroying them by using VIS/NIR diffuse reflectance spectroscopy combined with sample set partitioning based on joint x–y distances (SPXY), RF, and PLS algorithms. This study provides new technical means for fig products enterprises to determine the hardness of figs in the early stages of production rapidly and evaluate the processing quality of fig products, which has a high practical application potential.

Effect of gamma irradiation on physical, optical, spectroscopic and structural properties of Er3+-doped vitreous zinc borotellurite

In the present report, erbium doped zinc borotellurite glasses were irradiated with 50 kGy gamma radiation using a60Co radioisotope source. The effect of gamma irradiation on the physical, structural, optical and spectroscopic properties of erbium doped zinc borotellurite glasses was studied. Also the shielding properties of the un-irradiated glasses were studied with the help of Phy-X/PSD software. The gamma irradiation caused some structural changes as indicated by FTIR and Raman studies which may be due to the formation of the new structural units. Its influence on the optical properties like band gap (direct and indirect band gap), Urbach energy and refractive index were analyzed with the help of UV–Vis–NIR spectrometer. The UV–Vis–NIR absorption spectra showed increase in absorption after the gamma irradiation. The optical band gap value decreased for the irradiated samples. The spectroscopic properties such as stimulated emission cross-section, bandwidth, lifetime etc. Got marginally modified due to gamma irradiation. The shielding properties of the un-irradiated glasses were improved with the increasing rare earth concentration. The glasses with rare earth doping showed less changes in their properties after gamma irradiation which indicates that these glasses are more resistant to gamma radiation as compared to the base glass.

The Ariel Instrument Control Unit

Ariel, the Atmospheric Remote-sensing Infrared Exoplanet Large-survey mission (Tinetti 2019; Puig et al. 2018; Pascale et al. 2018), has been selected in March 2018 by ESA for the fourth medium-class mission (M4) launch opportunity of the Cosmic Vision Program, with an expected lift off in late 2028. It is the first mission dedicated to measuring the chemical composition and thermal structures of the atmospheres of hundreds of transiting exoplanets, enabling planetary science far beyond the boundaries of our own Solar System. Its Payload (P/L) (Eccleston and Tinetti 2018; Eccleston et al. 2017; Middleton et al. 2019), has been designed to perform transit spectroscopy from space during primary and secondary planetary eclipses in order to achieve a large unbiased survey concerning the nature of exoplanets atmospheres and their interiors, to determine the key factors affecting the formation and evolution of planetary systems (Tinetti et al. 2017, 2018). Ariel will observe hundreds of warm and hot transiting gas giants, Neptunes and super-Earths around a wide range of host star types, targeting planets hotter than $\sim $ 600 K to take advantage of their well-mixed atmospheres. It will exploit primary and secondary transit spectroscopy in the 1.10 to 7.80 μm spectral range and broad-band photometry in the optical (0.50 - 0.80 μm) and Near IR (0.80 - 1.10 μm) . One of the two instruments of the Ariel Payload is the Fine Guidance System (FGS), including three photometric channels (two used for guiding as well as science) between 0.5-1.1 μm plus a low resolution NIR spectrometer for 1.1-1.95 μm range. Along with FGS an IR Spectrometer (AIRS) (Amiaux et al. 2017) is foreseen, providing low-resolution spectroscopy in two IR channels: Channel 0 (CH0) for the 1.95 − 3.90 μm band and Channel 1 (CH1) for the 3.90 − 7.80 μm range. Finally, an Active Cooler System (ACS) including a Ne Joule-Thomson cooler is adopted to provide active cooling capability to the AIRS detectors working at cryogenic temperatures. AIRS is located at the intermediate focal plane of the telescope and common optical system and it hosts two HgCdTe-based hybrid IR detectors and two cold front-end electronics (CFEE) for detectors control and readout. Each CFEE is driven by a Detector Control Unit (DCU) part of AIRS but hosted within and managed by the Instrument Control Unit (ICU) of the Payload (Focardi et al. 2018). ICU is a warm unit residing into the S/C Service Module (SVM) and it is based on a cold redundant configuration involving the Power Supply Unit (PSU) and the Commanding and Data Processing Unit (CDPU) boards; both DCUs are instead cross-strapped and can be managed by the nominal or the redundant (PSU+CDPU) chain. ICU is in charge of AIRS management, collecting scientific and housekeeping (HK) telemetries from the spectrometer and HK from the telescope (temperatures readings), the P/L Optical Bench (OB) and other Subsystems (SS), thanks to a warm slave unit (TCU, Telescope Control Unit) interfaced to the ICU. Science and HK telemetries are then forwarded to the S/C, for temporary storage, before sending them to Ground. Here we describe the status of the ICU design at the end of B1 Phase, prior to the Mission Adoption Review (MAR) by ESA, with some still open architectural choices to be addressed and finalised once selected the ICU industrial Prime contractor.

Rapid and cost-effective nutrient content analysis of cotton leaves using near-infrared spectroscopy (NIRS).

The development of portable near-infrared spectroscopy (NIRS) combined with smartphone cloud-based chemometrics has increased the power of these devices to provide real-time in-situ crop nutrient analysis. This capability provides the opportunity to address nutrient deficiencies early to optimise yield. The agriculture sector currently relies on results delivered via laboratory analysis. This involves the collection and preparation of leaf or soil samples during the growing season that are time-consuming and costly. This delays farmers from addressing deficiencies by several weeks which impacts yield potential; hence, requires a faster solution. This study evaluated the feasibility of using NIRS in estimating different macro- and micronutrients in cotton leaf tissues, assessing the accuracy of a portable handheld NIR spectrometer (wavelength range of 1,350–2,500 nm). This study first evaluated the ability of NIRS to predict leaf nutrient levels using dried and ground cotton leaf samples. The results showed the high accuracy of NIRS in predicting essential macronutrients (0.76 ≤ R2 ≤ 0.98 for N, P, K, Ca, Mg and S) and most micronutrients (0.64 ≤ R2 ≤ 0.81 for Fe, Mn, Cu, Mo, B, Cl and Na). The results showed that the handheld NIR spectrometer is a practical option to accurately measure leaf nutrient concentrations. This research then assessed the possibility of applying NIRS on fresh leaves for potential in-field applications. NIRS was more accurate in estimating cotton leaf nutrients when applied on dried and ground leaf samples. However, the application of NIRS on fresh leaves was still quite accurate. Using fresh leaves, the prediction accuracy was reduced by 19% for macronutrients and 11% for micronutrients, compared to dried and ground samples. This study provides further evidence on the efficacy of using NIRS for field estimations of cotton nutrients in combination with a nutrient decision support tool, with an accuracy of 87.3% for macronutrients and 86.6% for micronutrients. This application would allow farmers to manage nutrients proactively to avoid yield penalties or environmental impacts.

Comparison of Dual Beam Dispersive and FTNIR Spectroscopy for Lactate Detection.

Near Infrared (800–2500 nm) spectroscopy has been extensively used in biomedical applications, as it offers rapid, in vivo, bed-side monitoring of important haemodynamic parameters, which is especially important in critical care settings. However, the choice of NIR spectrometer needs to be investigated for biomedical applications, as both the dual beam dispersive spectrophotomer and the FTNIR spectrometer have their own advantages and disadvantages. In this study, predictive analysis of lactate concentrations in whole blood were undertaken using multivariate techniques on spectra obtained from the two spectrometer types simultaneously and results were compared. Results showed significant improvement in predicting analyte concentration when analysis was performed on full range spectral data. This is in comparison to analysis of limited spectral regions or lactate signature peaks, which yielded poorer prediction models. Furthermore, for the same region, FTNIR showed 10% better predictive capability than the dual beam dispersive NIR spectrometer.

Testing the Limits of a Portable NIR Spectrometer: Content Uniformity of Complex Powder Mixtures Followed by Calibration Transfer for In-Line Blend Monitoring.

(1) Background: Portable NIR spectrometers gain more and more ground in the field of Process Analytical Technology due to the easy on-site flexibility and interfacing versatility. These advantages that originate from the instrument miniaturization, also come with a downside with respect to performance compared to benchtop devices. The objective of this work was to evaluate the performance of MicroNIR in a pharmaceutical powder blend application, having three active ingredients and 5 excipients. (2) Methods: Spectral data was recorded in reflectance mode using static and dynamic acquisition, on calibration set samples developed using an experimental design. (3) Results: The developed method accurately predicted the content uniformity of these complex mixtures, moreover it was validated in the entire calibration range using ±10% acceptance limits. With respect to at-line prediction, the method presented lower performance compared to a previously studied benchtop spectrometer. Regarding the in-line monitoring of the blending process, it was shown that the spectral variability-induced by dynamic acquisition could be efficiently managed using spectral pre-processing. (4) Conclusions: The in-line process monitoring resulted in accurate concentration profiles, highlighting differences in the mixing behaviour of the investigated ingredients. For the low dose component homogeneity was not reached due to an inefficient dispersive mixing.

Effect of vitamin-C on structural, thermal, and optical properties of lithium sulfate monohydrate crystal

A single crystal of vitamin-C-doped lithium sulfate (VCLSM) was grown at room temperature from aqueous solution by slow evaporation method. The as-grown crystals were found to be transparent and yellow in color. Powder X-ray diffractogram of the pure and doped crystals was recorded and various planes were identified for their corresponding reflections. The X-ray diffraction (XRD) analysis validated the crystalline nature of the samples. Single crystal X-ray diffraction study revealed the structure of the crystals and also revealed their monoclinic crystal system with space group P21. The vibrational modes of different functional groups present in the compound were analyzed using Fourier transform infrared (FTIR) spectral analysis. Thermal characteristics study of the crystal was carried out by thermogravimetric analysis (TGA) and differential thermal analysis (DTA) techniques, which clearly showed that the materials have high thermal stability even after doping. Optical transmittance spectrum using UV–Vis–NIR spectrometer showed that the grown crystal is highly transparent in the whole visible and Infrared (IR) region. Second-harmonic generation (SHG) property was confirmed for both pure and doped crystals by Kurtz’s powder test using Nd:YAG Laser.

Quantification of Silymarin in Silybi mariani fructus: Challenging the Analytical Performance of Benchtop vs. Handheld NIR Spectrometers on Whole Seeds.

The content of the flavonolignan mixture silymarin and its individual components (silichristin, silidianin, silibinin A, silibinin B, isosilibinin A, and isosilibinin B) in whole and milled milk thistle seeds (Silybi mariani fructus) was analyzed with near-infrared spectroscopy. The analytical performance of one benchtop and two handheld near-infrared spectrometers was compared. Reference analysis was performed with HPLC following a Soxhlet extraction (European Pharmacopoeia) and a more resource-efficient ultrasonic extraction. The reliability of near-infrared spectral analysis determined through partial least squares regression models constructed independently for the spectral datasets obtained by the three spectrometers was as follows. The benchtop device NIRFlex N-500 performed the best both for milled and whole seeds with a root mean square error of CV between 0.01 and 0.17%. The handheld spectrometer MicroNIR 2200 as well as the microPHAZIR provided a similar performance (root mean square error of CV between 0.01 and 0.18% and between 0.01 and 0.23%, respectively). We carried out quantum chemical simulation of near-infrared spectra of silichristin, silidianin, silibinin, and isosilibinin for interpretation of the results of spectral analysis. This provided understanding of the absorption regions meaningful for the calibration. Further, it helped to better separate how the chemical and physical properties of the samples affect the analysis. While the study demonstrated that milling of samples slightly improves the performance, it was deemed to be critical only for the analysis carried out with the microPHAZIR. This study evidenced that rapid and nondestructive quantification of silymarin and individual flavonolignans is possible with miniaturized near-infrared spectroscopy in whole milk thistle seeds.

Fast determination of three components in milk thistle extract with a hand-held NIR spectrometer and chemometrics tools

Milk thistle extract that is widely supplied in the market has three main active ingredients, including silybin, silychristin and silydianin (SS), and isosilybin, they have a lot of valuable functions for the human body. The determination of these active ingredients is of great significance for customers, so it is urgent to develop convenient, fast, and on-site analytical methods. In this study, we developed a fast method to detect the active ingredients using a hand-held NIR spectrometer MicroNIR 1700 to record spectra and applied multiple pretreatment methods to process them. The random frog (RG) method was used to optimize wavelength variables with high information, and the partial least square (PLS) regression was used to calibrate the spectra data of silybin, SS, and isosilybin. The results showed that the standard normal variate transformation (SNV), SNV+ first derivative, and first derivative were the best pretreatment methods for silybin, SS, and isosilybin. The calibration performances of selected wavelength variables were significantly improved compared to the whole wavelength variables. The the minimum limit of quantification (LOQmin) of the silybin, SS, and isosilybin were 9.53%, 10.86%, and 2.64%, respectively. For the validation of new unknown samples, the root mean squared error of prediction (RMSEP) of silybin, SS, and isosilybin were 0.666%, 0.510%, and 0.157%, and the corresponding R2 were 0.992, 0.898, and 0.936, respectively, it demonstrated that the proposed methods are accurate and robust. Benefiting from the portable hand-held NIR spectrometer and the reliable algorithm, the present method offers a promising approach for the determination of the active ingredients in milk thistle extract because it is rapid, convenient, non-polluting, and on-site, does not require professional skills and expensive apparatus.

Investigation on spectral standardization among multi-channel of an on-line near-infrared spectrometer

To tackle the issue that the multivariate calibration model cannot be shared among channels due to the inter-channel spectral differences in the on-line multi-channel spectrometer, taking the determination of octane number (RON) and aromatic hydrocarbon content (AC) of multiple reformulated gasoline by on-line Fourier transform near-infrared (FT-NIR) spectrometer combined with partial least squares (PLS) as an example, a novel approach for spectral standardization by piecewise cosine spectra (cosSSM) is proposed in this paper. A total of 42 reformulated gasoline samples were collected and their RON and AC were determined using standard methods with their NIR absorption spectra being measured using the three channels of an on-line NIR spectrometer, respectively. To prove the effectiveness of cosSSM, the spectra of each channel were standardized using cosSSM, and the PLS models of RON and AC were established using the cosine spectra in one channel, then the predictions of the cosine spectra in the other channels were made. The results suggested that cosSSM effectively eliminated the adverse effect of inter-channel spectral variation on the applicability of the channel-specific PLS model, and the performance was comparable to that of the well-established piecewise direct standardization (PDS) method. Besides, cosSSM is relatively simple and easy-to-implement in practical application.

Authenticity Identification of Copaiba Oil Using a Handheld NIR Spectrometer and DD-SIMCA

Copaiba oil (CO) is a non-timber forest product, obtained from the Copaifera genus, used in popular medicine, pharmaceutical, and cosmetic industries. The increasing demand and price of this oil also increased the occurrence of adulterations in commercial products and small providers in cooperatives. Nowadays, the GC-MS is the most suitable method used for quality control. However, the high cost of this technique is unfeasible for small traders and cooperatives. Considering the lack of alternative methods for CO analysis, this work presents an easy, direct, fast, and inexpensive method based on a handheld NIR spectrometer and a DD-SIMCA model for purity authentication. The training set was composed of 65 pure CO samples originated by 12 distinct sources of Copaifera sp. oil, 9 distinct commercial samples, binary and ternary mixtures among them. The validation was accomplished using 213 samples constituted by independent authentic samples, samples submitted to thermal stress, commercial samples with and without edible vegetable oil adulteration, and samples purposefully adulterated with edible oils (palm, olive, sunflower, coconut, and soybean). The optimized DD-SIMCA model showed 95% of sensitivity regarding the target class, where 5% the false-negative rate revealed the necessity of increasing the number of commercial samples in the training set. The specificity, related to samples not belonging to the target class, was 97.3%, showing that the model is highly effective to detect adulterated samples. The average efficiency of the method was 92.3%, revealing to be very promising for quality control and authenticity testing applications in small cooperatives.

Comparing the Use of Handheld and Benchtop NIR Spectrometers in Predicting Nutritional Value of Forage

HighlightsA transportable spectrometer performed similarly to benchtop spectrometers to predict forage quality.Absorption bands in the second and third overtone regions were used to predict forage quality with ample accuracy.A low-cost handheld spectrometer was useful for routine screening of forage for nitrogen content. Abstract. Assessing the nutritional composition of animal feed and forage materials is important to achieve high animal productivity and wellness. Precision nutrition programs that use NIR technology can determine the nutritional composition of feed and forage quickly and simply, generating actionable information such as total nitrogen (N), acid detergent fiber (ADF), neutral detergent fiber (NDF), and acid detergent lignin (ADL) contents, as well as in vitro dry matter digestibility (IVDMD). Recent advances in optics and microelectronics have allowed for the development of handheld spectrometers that are portable, robust, and user-friendly. However, are the handheld units accurate enough to predict nutritional content of animal feed? In this study, the performance of two handheld NIR spectrometers to predict the nutritional content of forage based on N, ADF, NDF, ADL, and IVDMD was evaluated by comparing them to two benchtop NIR spectrometers often used in feed and forage analysis. The forage samples comprised switchgrass (Panicum virgatum L), big bluestem (Andropogon gerardi), and Indiangrass (Sorghastrum nutans). The first handheld spectrometer covers 780-2500 nm with a spectral interval (??) of 1 nm, while the second handheld spectrometer is a palm-sized smartphone spectrometer covering 900-1700 nm with ?? = 4 nm. The benchtop spectrometers both cover 400-2500 nm with ?? = 2 nm. Forage samples were scanned on each spectrometer and divided into calibration (n = 143) and validation (n = 35) sets. Partial least squares (PLS) regression was used to calibrate all spectrometers using mean-centered spectral data that had been preprocessed using Savitzky-Golay first derivative (SG1) or second derivative (SG2) algorithm with 9-63 smoothing points. Results showed that PLS models that best predicted N using the benchtop spectrometers had lower standard error of prediction (SEP = 1.24-1.28 g kg-1) and higher ratio of prediction to deviation (RPD = 3.66-3.78) compared to the models developed based on spectra collected from the handheld spectrometers (SEP = 1.46-1.78 g kg-1; RPD = 2.39-2.84). ADF, NDF, and ADL were variable and generally poorly predicted using spectra from the benchtop spectrometers (SEP = 10.02-33.19 g kg-1;RPD = 1.71-2.24), and even more so using the handheld spectrometers (SEP = 10.63-32.57 g kg-1;RPD = 1.64-2.47). Predicting IVDMD was similar for both sets of benchtop (SEP = 40.00-41.73 g kg-1; RPD = 2.24-2.34) and handheld (SEP = 34.46-40.84 g kg-1; RPD = 2.29-2.72) spectrometers. These results show that the handheld devices can be used for screening of forage samples based on N, which is a closely monitored component in animal feed and forage, as well as IVDMD, an important forage quality index. Keywords: Forage, Portable spectrometer, Spectroscopy, Screenin, Ruminant nutrition.

Comparison of smartphone and lab-grade NIR spectrometers to measure chemical composition of lamb and beef

Context Near-infrared reflectance spectroscopy (NIRS) has been extensively investigated for non-destructive and rapid determination of pH and chemical composition of meat including water, crude protein, intramuscular fat (IMF) and stable isotopes. Smaller, cheaper NIRS sensors that connect to a smartphone could enhance the accessibility and uptake of this technology by consumers. However, the limited wavelength range of these sensors could restrict the accuracy of predictions compared with benchtop laboratory NIRS models. Aims To compare the precision and accuracy metrics of predicting pH, water, crude protein and IMF of three sample presentations and two sensors. Methods Fresh intact (FI) store-bought beef and lamb steak samples (n = 43) were ground and freeze-dried (FD), and then oven-dried to create freeze-dried oven-dried (FDOD) samples. All three forms of sample presentation (FI, FD, FDOD) were scanned using the smartphone and benchtop NIRS sensors. Key results The IMF was the best predicted trait in FD and FDOD forms by the smartphone NIRS (R2 &gt;0.75; RPD &gt;1.40) with limited differences between the two sensors. However, predictions on FI meat were poorer for all traits regardless of the NIRS scanner used (R2 ≤ 0.67; RPD ≤ 1.58) and not suitable for use in research or industry. Conclusion The smartphone NIRS sensor showed accuracy and precision comparable to benchtop NIRS to predict meat composition. However, these preliminary results found that neither of the two sensors reliably predicted quality attributes for industry or consumer applications. Implications Miniaturised NIRS sensors connected to smartphones could provide a practical solution to measure some meat quality attributes such as IMF, but the accuracy depends on sample presentation.

Rapid and Nondestructive On-Site Classification Method for Consumer-Grade Plastics Based on Portable NIR Spectrometer and Machine Learning

The classification of plastic waste before recycling is of great significance to achieve effective recycling. In order to achieve rapid, nondestructive, and on-site detection, a portable near-infrared spectrometer was used in this study to obtain the diffuse reflectance spectrum for both standard and commercial plastics made by ABS, PC, PE, PET, PP, PS, and PVC. After applying a series of pretreatments, the principal component analysis (PCA) was used to analyze the cluster trend. K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural network (BPNN) classification models were developed and evaluated, respectively. The result showed that different plastics could be well separated in top three principal components space after pretreatment, and the classification models performed excellent classification results and high generalization capability. This study indicated that the portable NIR spectrometer, integrated with chemometrics, could achieve excellent performance and has great potential in the field of commercial plastic identification.

Comparison of Vis/SWNIR and NIR spectrometers combined with different multivariate techniques for estimating soil fertility parameters of calcareous topsoil in an arid climate

The lab spectroscopy using Vis/SWNIR and NIR spectrometers was used to estimate the major fertility parameters in calcareous topsoil from arid region of central Iran. The fertility parameters included soil organic matter (SOM), total nitrogen (TN), available phosphorus (P -avl ), and available potassium (K -avl ). A total of 151 samples were collected from the depth of 0–20 cm, and the diffuse reflectance spectra of them were collected using charge-coupled device (CCD) and indium-gallium-arsenide (InGaAs) spectrometers. To extract the prediction models, partial least squares (PLS) as linear, and artificial neural networks (ANN) and support vector machines (SVM) as nonlinear multivariate techniques were used. The CCD-based spectra combined with the ANN model led to excellent predictability for SOM (residual predictive deviation (RPD) = 2.60), TN (RPD = 2.54), and P -avl (RPD = 3.01) and very good prediction for K -avl (RPD = 2.02). However, the InGaAs-based spectrometer outperformed the CCD one for predicting all the soil fertility parameters. The best prediction models were obtained from the nonlinear multivariate techniques where the excellent predictions were provided by the ANN model for SOM ( R 2 p = 0.941; RPD = 3.27), P -avl ( R 2 p = 0.902; RPD = 3.34), and K -avl ( R 2 p = 0.863; RPD = 2.69) and by SVM for TN ( R 2 p = 0.863; RPD = 2.91). This study revealed that despite the great potential of InGaAs spectrometers for predicting the soil fertility parameters, the low-cost CCD-based spectrometer could result in satisfactory performance when the appropriate nonlinear technique is used for data analysis. • The potential of two spectrometers was compared to estimate fertility parameters. • Linear and nonlinear techniques were used to extract the prediction models. • CCD spectrometer combined with the ANN model led to satisfactory performance. • Nonlinear multivariate techniques resulted in the best accuracy.

Assessing the Quality of Milk Using a Multicomponent Analytical Platform MicroNIR/Chemometric

In this work, an innovative screening platform based on MicroNIR and chemometrics is proposed for the on-site and contactless monitoring of the quality of milk using simultaneous multicomponent analysis. The novelty of this completely automated tool consists of a miniaturized NIR spectrometer operating in a wireless mode that allows samples to be processed in a rapid and accurate way and to obtain in a single click a comprehensive characterization of the chemical composition of milk. To optimize the platform, milk specimens with different origins and compositions were considered and prediction models were developed by chemometric analysis of the NIR spectra using Partial Least Square regression algorithms. Once calibrated, the platform was used to predict samples acquired in the market and validation was performed by comparing results of the novel platform with those obtained from the chromatographic analysis. Results demonstrated the ability of the platform to differentiate milk as a function of the distribution of fatty acids, providing a rapid and non-destructive method to assess the quality of milk and to avoid food adulteration.