Allan Variance Characterization of Compact Fourier Transform Infrared Spectrometers

Handheld Fourier transform infrared (FT-IR) spectrometers are very promising candidates for several applications where accurate real-time material detection and quantification are needed. Due to their compact size, their mode of operation which does not allow for long warm-up time, and changing environmental conditions, these spectrometers suffer from short-term noise and long-term instabilities which affect their performance. In this work, the effect of long-term multiplicative instabilities on the signal-to-noise ratio (S/N), measured using the 100% line-method, is studied. An expression for the variance, in this case, is deduced. The Allan variance technique is used to identify and quantify the presence of the different types of noises. The methodology is applied to a commercial NeoSpectra scanner module from Si-Ware Systems, Inc.

Impact of Global Climate Change on the European Barley Market Requires Novel Multi-Method Approaches to Preserve Crop Quality and Authenticity

Most recently in 2018 and 2019, large parts of Europe were affected by periods of massive drought. Resulting losses in cereal yield pose a major risk to the global supply of barley, as more than 60% of global production is based in Europe. Despite the arising price fluctuations on the cereal market, authenticity of the crop must be ensured, which includes correct declaration of harvest years. Here, we show a novel approach that allows such differentiation for spring barley samples, which takes advantage of the chemical changes caused by the extreme drought. Samples from 2018 were successfully differentiated from those of 2017 by analysis of changes in near-infrared spectra, enrichment in the isotope ¹³C, and strong accumulation of the plant-physiological marker betaine. We demonstrate that through consideration of multiple modern analysis techniques, not only can fraudulent labelling be prevented, but indispensable knowledge on the drought tolerance of crops can be obtained.

Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging

Optical sensors combined with machine learning algorithms have led to significant advances in seed science. These advances have facilitated the development of robust approaches, providing decision-making support in the seed industry related to the marketing of seed lots. In this study, a novel approach for seed quality classification is presented. We developed classifier models using Fourier transform near-infrared (FT-NIR) spectroscopy and X-ray imaging techniques to predict seed germination and vigor. A forage grass (Urochloa brizantha) was used as a model species. FT-NIR spectroscopy data and radiographic images were obtained from individual seeds, and the models were created based on the following algorithms: linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), random forest (RF), naive Bayes (NB), and support vector machine with radial basis (SVM-r) kernel. In the germination prediction, the models individually reached an accuracy of 82% using FT-NIR data, and 90% using X-ray data. For seed vigor, the models achieved 61% and 68% accuracy using FT-NIR and X-ray data, respectively. Combining the FT-NIR and X-ray data, the performance of the classification model reached an accuracy of 85% to predict germination, and 62% for seed vigor. Overall, the models developed using both NIR spectra and X-ray imaging data in machine learning algorithms are efficient in quickly, non-destructively, and accurately identifying the capacity of seed to germinate. The use of X-ray data and the LDA algorithm showed great potential to be used as a viable alternative to assist in the quality classification of U. brizantha seeds.

Authenticity identification and classification of Rhodiola species in traditional Tibetan medicine based on Fourier transform near-infrared spectroscopy and chemometrics analysis

Rhodiola is an increasingly widely used traditional Tibetan medicine and traditional Chinese medicine in China. The composition profiles of bioactive compounds are somewhat jagged according to different species, which makes it crucial to identify authentic Rhodiola species accurately so as to ensure clinical application of Rhodiola. In this paper, a nondestructive, rapid, and efficient method in classification of Rhodiola was developed by Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics analysis. A total of 160 batches of raw spectra were obtained from four different species of Rhodiola by FT-NIR, such as Rhodiola crenulata, Rhodiola fastigiata, Rhodiola kirilowii, and Rhodiola brevipetiolata. After excluding the outliers, different performances of 3 sample dividing methods, 12 spectral preprocessing methods, 2 wavelength selection methods, and 2 modeling evaluation methods were compared. The results indicated that this combination was superior than others in the authenticity identification analysis, which was FT-NIR combined with sample set partitioning based on joint x-y distances (SPXY), standard normal variate transformation (SNV) + Norris-Williams (NW) + 2nd derivative, competitive adaptive reweighted sampling (CARS), and kernel extreme learning machine (KELM). The accuracy (ACCU), sensitivity (SENS), and specificity (SPEC) of the optimal model were all 1, which showed that this combination of FT-NIR and chemometrics methods had the optimal authenticity identification performance. The classification performance of the partial least squares discriminant analysis (PLS-DA) model was slightly lower than KELM model, and PLS-DA model results were ACCU = 0.97, SENS = 0.93, and SPEC = 0.98, respectively. It can be concluded that FT-NIR combined with chemometrics analysis has great potential in authenticity identification and classification of Rhodiola, which can provide a valuable reference for the safety and effectiveness of clinical application of Rhodiola.

Long-Length Fiber Optic Near-Infrared (NIR) Spectroscopy Probes for On-Line Quality Control of Processed Land Animal Proteins

This research was conducted using a spectral database comprising 346 samples of processed animal proteins (PAPs) with a range of compositions, analyzed using a Fourier transform near-infrared spectroscopy multichannel instrument (Matrix-F, Bruker Optics) coupled to a 100 m fiber optic cable. Using both its static and dynamic operating modes (on a conveyor belt), simulating the movement of the product in the plant, the predictive capabilities of both modes of analysis were assessed and compared, for the purposes of predicting moisture, protein, and ashes. The results show that both exhibit highly similar degrees of precision and accuracy for predicting these parameters. This research provides a foundation of scientific-technical knowledge, hitherto unknown, regarding the "on-line" incorporation of an instrument (equipped with a 100 m fiber optic cable) into a processing plant of by-products of animal origin.

Evolution of Frying Oil Quality Using Fourier Transform Near-Infrared (FT-NIR) Spectroscopy

This study assesses the capacity of a Fourier transform near-infrared (FT-NIR) spectrometer operating in the range 4500-12 000 cm^-1 (833.33-2222.22 nm) to provide quantitative predictions for the parameters of acidity (AV), p-anisidine (pAV), total polar materials (TPM), peroxide value (PV), and oxidative stability index (OSI). 562 samples of frying oil were analyzed from 14 distinct types of oil. The calibrations obtained accounted for 96%, 95%, 99%, 92%, and 91% of the AV, pAV, TPM, PV, and OSI variations in the study set and the similarity between the standard error of laboratory (RMSEP) values and the reference method errors (RMSEL), enabling the authors to conclude that NIR technology has the capacity to replace traditional methods in thermo-oxidative degradation studies in frying oils.