Standard Normal Variate Transformation Research Articles

Potential of two-dimensional correlation-based dual-band visible/near infrared spectroscopy to predict total volatile basic nitrogen content in meat

In the meat industry, it is essential to monitor the total volatile basic nitrogen (TVB-N) content due to its impact on human diets safety. In the present study, two-dimensional correlation spectroscopy (2D-COS) was combined with dual-band visible/near-infrared spectroscopy to identify the characteristic wavelengths of TVB-N in pork meat during storage. Reflectance spectra of pork longissimus dorsi muscles at 350–1,100nm and 1,000–2,500nm were obtained during a 13-day storage period. The dual-band spectra were merged via parabolic fitting, and subjected to pre-treatments using absorbance conversion, first derivation (FD), and standard normal variate transformation (SNV). Then, the storage day was used as the external perturbation factor, and 2D-COS was performed on the dynamic spectra to identify TVB-N-related wavebands. Twenty-five characteristic wavelengths were selected, and a simplified partial least square regression (PLSR) model was established with a correlation coefficient (Rp) of 0.9591, root mean square error on prediction (RMSEP) of 1.8341mg/100g, and relative percent deviation (RPD) of 3.2518. These results outperformed those of the model based on full wavebands data (Rp = 0.9215, RMSEP = 2.8191mg/100g, RPD = 2.1114). This study accurately predicted TVB-N values in pork meat, and the integration of 2D-COS and dual-band spectra had great potential for real-time meat freshness evaluation.

Discrimination of New and Aged Seeds Based on On-Line Near-Infrared Spectroscopy Technology Combined with Machine Learning

The harvest year of maize seeds has a significant impact on seed vitality and maize yield. Therefore, it is vital to identify new seeds. In this study, an on-line near-infrared (NIR) spectra collection device (899–1715 nm) was designed and employed for distinguishing maize seeds harvested in different years. Compared with least squares support vector machine (LS-SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM), the partial least squares discriminant analysis (PLS-DA) model has the optimal recognition performance for maize seed harvest years. Six different preprocessing methods, including Savitzky–Golay smoothing (SGS), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1 derivative (SG-D1), Savitzky–Golay 2 derivative (SG-D2), and normalization (Norm), were used to improve the quality of the spectra. The Monte Carlo cross-validation uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), successive projections algorithm (SPA), and their combinations were used to obtain effective wavelengths and decrease spectral dimensionality. The MC-UVE-BOSS-PLS-DA model achieved the classification with an accuracy of 88.75% using 93 features based on Norm preprocessed spectral data. This study showed that the self-designed NIR collection system could be used to identify the harvested years of maize seed.

Automated classification pipeline for real-time in vivo examination of colorectal tissue using Raman spectroscopy

Colorectal cancer is the third most common malignancy worldwide and one of the leading causes of death in oncological patients with its diagnosis typically involving confirmation by tissue biopsy. In vivo Raman spectroscopy, an experimental diagnostic method less invasive than a biopsy, has shown great potential to discriminate between normal and cancerous tissue. However, the complex and often manual processing of Raman spectra along with the absence of a suitable instant classifier are the main obstacles to its adoption in clinical practice. This study aims to address these issues by developing a real-time automated classification pipeline coupled with a user-friendly application tailored for non-spectroscopists. First, in addition to routine colonoscopy, 377 subjects underwent in vivo acquisitions of Raman spectra of healthy tissue, adenomatous polyps, or cancerous tissue, which were conducted using a custom-made microprobe. The spectra were then loaded into the pipeline and pre-processed in several steps, including standard normal variate transformation and finite impulse response filtration. The quality of the pre-processed spectral data was checked based on their signal-to-noise ratio before the suitable spectra were decomposed and classified using a combination of principal component analysis and a support vector machine, respectively. After five-fold cross-validation, the developed classifier exhibited 100% sensitivity toward adenocarcinoma and adenomatous polyps. The overall accuracy was 96.9% and 79.2% for adenocarcinoma and adenomatous polyps respectively. In addition, an application with a graphical user interface was developed to facilitate the use of our data pipeline by medical professionals in a clinical environment. Overall, the combination of supervised and unsupervised machine learning with algorithmic pre-processing of in vivo Raman spectra appears to be a viable way of reducing the relatively large number of biopsies currently needed to definitively diagnose colorectal cancer.

Application of hyperspectral imaging and colorimetric sensor array coupled with multivariate analysis for quality detection during salted duck eggs processing

AbstractIn order to realize the evaluation of salted duck eggs quality, this study constructed salts‐sensitive colorimetric sensor array (CSA) based on silver nanoprisms (AgNPRs) etching principle and utilized hyperspectral imaging (HSI) system to capture the reflectance spectral of CSA and section of salted duck eggs. To minimize the noise in spectral signals, three preprocessing methods including Savitzky–Golay Smoothing (SG), standard normal variate (SNV) transformation, and multiplicative scatter correction were used. The competitive adaptive reweighted sampling and uninformative variables elimination were used to select the optimal wavelengths, and then partial least squares regression was used to predict moisture, lipid, and salt content (SC). Results demonstrated coefficients of determination of 0.8200, 0.8969, 0.8709, and 0.9239 for protein moisture, yolk moisture, yolk lipid, and protein SC. Visualization distribution maps successfully display the distribution of moisture and lipid content during salting periods. The results indicated that the CSA based on AgNPRs etching principle can be an effective tool in quantitative measurement of salt and HSI combining CSA can be applied to evaluate the quality of salted duck eggs comprehensively. The study offers a rapid detection method for the production and processing of salted duck eggs in the industry.Practical applicationA novel salts‐sensitive CSA was constructed to evaluate quality of salted duck eggs based on AgNPRs etching. This study designed and developed a portable sample collection device using 3D printing technology to acquire the information of samples and CSA at the same time. This study combined with HSI system and colorimetric sensor technology to evaluate the quality of salted duck eggs during the salting process.

Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy

Widespread adoption of machine-picked cotton in China, the impurity content of seed cotton has increased significantly. This impurity content holds direct implications for the valuation of seed cotton and exerts a consequential influence on the ensuing quality of processed lint and textiles. Presently, the primary approach for assessing impurity content in seed cotton primarily depends on semi-automated testing instruments, exhibiting suboptimal detection efficiency and not well-suited for the impurity detection requirements during the purchase of seed cotton. To address this challenge, this study introduces a seed cotton near-infrared spectral (NIRS) data acquisition system, facilitating the rapid collection of seed cotton spectral data. Three pretreatment algorithms, namely SG (Savitzky-Golay convolutional smoothing), SNV (Standard Normal Variate Transformation), and Normalization, were applied to preprocess the seed cotton spectral data. Cotton-Net, a one-dimensional convolutional neural network aligned with the distinctive characteristics of the seed cotton spectral data, was developed in order to improve the prediction accuracy of seed cotton impurity content. Ablation experiments were performed, utilizing SELU, ReLU, and Sigmoid functions as activation functions. The experimental outcomes revealed that after normalization, employing SELU as the activation function led to the optimal performance of Cotton-Net, displaying a correlation coefficient of 0.9063 and an RMSE (Root Mean Square Error) of 0.0546. In the context of machine learning modeling, the LSSVM model, developed after Normalization and Random Frog algorithm processing, demonstrated superior performance, achieving a correlation coefficient of 0.8662 and an RMSE of 0.0622. In comparison, the correlation coefficient of Cotton-Net increased by 4.01%. This approach holds significant potential to underpin the subsequent development of rapid detection instruments targeting seed cotton impurities.

Determination of soluble solids content of multiple varieties of tomatoes by full transmission visible-near infrared spectroscopy.

Soluble solids content (SSC) is a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming. To enhance accuracy and efficiency in SSC assessment, this study employs full transmission visible and near-infrared (Vis-NIR) spectroscopy and multi-point spectral data collection techniques to quantitatively analyze SSC in two tomato varieties ('Provence' and 'Jingcai No.8' tomatoes). Preprocessing of the multi-point spectra is carried out using a weighted averaging approach, aimed at noise reduction, signal-to-noise ratio improvement, and overall data quality enhancement. Taking into account the potential influence of various detection orientations and preprocessing methods on model outcomes, we investigate the combination of partial least squares regression (PLSR) with two orientations (O1 and O2) and two preprocessing techniques (Savitzky-Golay smoothing (SG) and Standard Normal Variate transformation (SNV)) in the development of SSC prediction models. The model achieved the best results in the O2 orientation and SNV pretreatment as follows: 'Provence' tomato (Rp = 0.81, RMSEP = 0.69°Brix) and 'Jingcai No.8' tomatoes (Rp = 0.84, RMSEP = 0.64°Brix). To further optimize the model, characteristic wavelength selection is introduced through Least Angle Regression (LARS) with L1 and L2 regularization. Notably, when λ=0.004, LARS-L1 produces superior results ('Provence' tomato: Rp = 0.95, RMSEP = 0.35°Brix; 'Jingcai No.8' tomato: Rp = 0.96, RMSEP = 0.33°Brix). This study underscores the effectiveness of full transmission Vis-NIR spectroscopy in predicting SSC in different tomato varieties, offering a viable method for accurate and swift SSC assessment in tomatoes.

Stratified diagnosis of cotton canopy spectral characteristics based on CWT-SPA and its relationship with moisture, nitrogen, and SPAD values

ABSTRACT Cotton is an economically valuable crop worldwide, and accurate and rapid leaf moisture content, nitrogen content, and soil plant analysis development (SPAD) value estimations are crucial for cotton growth. In this study, high-resolution spectral data were collected for the Tahe 2 cotton variety in Alar City, Xinjiang. The raw spectra were preprocessed using four methods: first derivative, standard normal variate transformation, second derivative, and multiplicative scatter correction. Wavelet coefficients at multiple scales were generated from the raw spectra using a continuous wavelet transform (CWT) and CWT with first derivative (CWT-FD). A quantitative detection model combining CWT and CWT-FD with successive projection algorithm – partial least squares (SPA-PLS) was developed to estimate the leaf moisture content, nitrogen content, and SPAD values. The performances of the different scales and methods for estimating these parameters were compared. Six stratification methods were employed to determine the optimal modelling results and decomposition scales for the parameters under various stratification schemes. The results demonstrated that the CWT and CWT-FD methods significantly improved the accuracy of estimating cotton canopy spectral characteristics related to the parameters, surpassing that of conventional spectral processing methods. The mexh, db5, and db5 wavelet basis functions, along with scale 3, were identified as the optimal modelling choices for moisture content, nitrogen content, and SPAD values throughout the cotton plant growth period. Among the six vertical stratification structures of cotton, the CWT and CWT-FD methods, along with the three wavelet basis functions, achieved the best results for the three parameters in the upper layer. The optimal relative percent deviation values for the three parameters of typical leaves ranged from 1.603 to 2.016, indicating satisfactory modelling performance. These findings provide a theoretical foundation for accurately estimating leaf moisture content, nitrogen content, and SPAD values using spectral technology, enabling the simultaneous extraction and monitoring of multiple physiological indicators.

Comparison of principal component regression (PCR) and partial least square regression (PLSR) modeling methods for quantifying polyethylene (PE) in recycled polypropylene (rPP) with near-infrared spectrometry (NIR)

Recycled polypropylene (rPP) often contains a small amount of polyethylene (PE). Since polypropylene (PP) and PE are incompatible, the presence of PE compromises the performance of rPP materials and needs to be closely monitored. In our previous work, Raman and near-infrared (NIR) spectrometries were evaluated to monitor PE content in rPP with partial least square regression (PLSR) modeling. The NIR spectrometry exhibited a wider application range, but the accuracy of the prediction models might be further improved. In the current work, a different modeling method, principal component regression (PCR) was employed to analyze PE content in rPP with NIR spectrometry. Spectrum pretreatment methods, including multivariate scatter correction (MSC), standard normal variate transformation (SNV), smoothing, and first derivative, were investigated to improve the NIR spectrum quality. Forward and backward interval methods were used to optimize spectral range selection. The outcomes were compared with our previous PLSR modeling results. The highest accuracy in independent validation was achieved by a PCR model with an R 2 of 0.9991 and a root-mean-square error of prediction (RMSEP) of 0.1596 PE%. On the other hand, a PLSR model achieved the lowest RMSEP of 0.9712 PE% for a non-colored post-consumer rPP sample. The PCR models might be sensitive to interference and more suitable for post-industrial materials, which have a simpler chemical composition. The PLSR models might have better stability and be more suitable for complicated post-consumer samples. Both the PCR and PLSR models were successfully applied to a gray commercial rPP sample.

Improving the estimation accuracy of rapeseed leaf photosynthetic characteristics under salinity stress using continuous wavelet transform and successive projections algorithm.

Soil salinization greatly restricts crop production in arid areas for salinity stress can inhibit crop photosynthesis and growth. Chlorophyll fluorescence and photosynthetic gas exchange (CFPGE) parameters are important indicators of crop photosynthesis and have been widely used to evaluate the impacts of salinity stress on crop photosynthesis and growth. Remote sensing technology can quickly and non-destructively obtain crop information under salinity stress, however, at present, the distribution of spectral features of CFPGE parameters in different regions is still unclear. In this study (2019-2020), under salinity stress conditions, the spectral data of rapeseed leaves were acquired and the CFPGE parameters were simultaneously determined. Then, continuous wavelet transformation (CWT) and standard normal variate (SNV) transformation were utilized to preprocess the raw spectral data. After that, a CFPGE parameter estimation model was constructed by using the partial least squares regression (PLSR) algorithm and the support vector machines (SVM) algorithm based on the spectral features in the red region (600-800 nm) and those in the red, blue-green (350-600 nm), and near-infrared (800-2500 nm) regions. The results showed that the spectral features of CFPGE parameters could be extracted by successive projections algorithm (SPA) based on the CWT preprocessing. The CFPGE parameter estimation model constructed based on the spectral features in the red region (675 nm, 680 nm, 688 nm, 749 nm, and 782 nm) had the highest Fv/Fm estimation accuracy on day 30, with R2c, R2p, and RPD of 0.723, 0.585, and 1.68, respectively. Based on this, the spectral features (578 nm, 976 nm, 1088 nm, 1476 nm, and 2250 nm) in the blue-green and near-infrared regions were added in the variables for modeling, which significantly improved the accuracy and stability of the model, with R2c, R2p, and RPD of 0.886, 0.815, and 2.58, respectively. Therefore, the fusion of the spectral features in the red, blue-green, and near-infrared regions could improve the estimation accuracy of rapeseed leaf CFPGE parameters. This study will provide technical reference for rapid estimation of photosynthetic performance of crops under salinity stress in arid and semi-arid areas.

Inversion of soil water and salt information based on UAV hyperspectral remote sensing and machine lear-ning.

Accurate diagnosis of water and salt information in saline agricultural lands is crucial for long-term soil quality improvement and arable land conservation. In this study, we extracted field-scale vegetation canopy spectral information by UAV hyperspectral information, transforming the reflectance (R) to standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative of reflectance (FDR) and second derivative of reflectance (SDR). We determined the optimal spectral transformation forms of soil water content (SWC), soil pH, and soil salt content (SSC) by the maximum absolute correlation coefficient (MACC), and extracted the feature bands by competitive adaptive reweighted sampling (CARS). We constructed an inversion model of soil water and salt information by partial least squares regression (PLSR), random forest (RF), and extreme gradient boosting (XGBoost). The results showed that R, FDR and MSC were the best spectral transformation types for soil water content, soil pH, and soil salt content, and the corresponding MACC were 0.730, 0.472 and 0.654, respectively. The CARS algorithm effectively eliminated the irrelevant variables, optimally selecting 16-17 feature bands from 150 spectral bands. Both soil water content and soil pH performed best with XGBoost model, achieving determination coefficient of validation (Rp2) 0.927 and 0.743, and the relative percentage difference (RPD) amounted to 3.93 and 2.45. For soil salt content, the RF model emerged as the best inversion method with Rp2 and RPD of 0.427 and 1.64, respectively. The study could provide a reference solution for the integrated remote sensing monitoring of soil water and salt information in space and sky, serving as a scientific guide for the amelioration and sustainable management of saline lands.

A methodological approach to preprocessing FTIR spectra of adulterated sesame oil.

Fourier transform infrared (FTIR) spectroscopy is established as an effective and fast method for the confirmation of the authenticity of food and among other, edible oils. However, no standard procedure is available for applying preprocessing as a vital step in obtaining accurate results from spectra. This study proposes a methodological approach to preprocessing FTIR spectra of sesame oil adulterated with vegetable oils (canola oil, corn oil, and sunflower oil). The primary preprocessing methods investigated are orthogonal signal correction (OSC), standard normal variate transformation (SNV), and extended multiplicative scatter correction (EMSC). Other preprocessing methods are used both as standalone methods and in combination with the primary preprocessing methods. The preprocessing results are compared using partial least squares regression (PLSR). OSC alone or with detrending were the most accurate in predicting the adulteration level of sesame oil, with a maximum coefficient of prediction (R2p) range of 0.910 to 0.971 for different adulterants.

Total Soluble Solids in Grape Must Estimation Using VIS-NIR-SWIR Reflectance Measured in Fresh Berries

Total soluble solids (TSS) is a key variable taken into account in determining optimal grape maturity for harvest. In this work, partial least square (PLS) regression models were developed to estimate TSS content for Godello, Verdejo (white), Mencía, and Tempranillo (red) grape varieties based on diffuse spectroscopy measurements. To identify the most suitable spectral range for TSS prediction, the regression models were calibrated for four datasets that included the following spectral ranges: 400–700 nm (visible), 701–1000 nm (near infrared), 1001–2500 nm (short wave infrared) and 400–2500 nm (the entire spectral range). We also tested the standard normal variate transformation technique. Leave-one-out cross-validation was implemented to evaluate the regression models, using the root mean square error (RMSE), coefficient of determination (R2), ratio of performance to deviation (RPD), and the number of factors (F) as evaluation metrics. The regression models for the red varieties were generally more accurate than the models of those for the white varieties. The best regression model was obtained for Mencía (red): R2 = 0.72, RMSE = 0.55 °Brix, RPD = 1.87, and factors n = 7. For white grapes, the best result was achieved for Godello: R2 = 0.75, RMSE = 0.98 °Brix, RPD = 1.97, and factors n = 7. The methodology used and the results obtained show that it is possible to estimate TSS content in grapes using diffuse spectroscopy and regression models that use reflectance values as predictor variables. Spectroscopy is a non-invasive and efficient technique for determining optimal grape maturity for harvest.

Spectral separation degree method for Vis-NIR spectroscopic discriminant analysis of milk powder adulteration

Adulteration detection of adding ordinary milk powder to high-end dedicated milk powder is challenging due to the high similarity. Using visible and near-infrared (Vis-NIR) spectroscopy combined with k-nearest neighbor (kNN), the discriminant analysis models of pure brand milk powder and its adulterated milk powder (including unary and binary adulteration) were established. Standard normal variate transformation and Norris derivative filter (D = 2, S = 11, G = 5) were jointly used for spectral preprocessing. The separation degree and separation degree spectrum between two spectral populations were proposed and used to describe the differences between the two spectral populations, based on which, a novel wavelength selection method, named separation degree priority combination-kNN (SDPC-kNN), was proposed for wavelength optimization. SDPC-wavelength step-by-step phase-out-kNN (SDPC-WSP-kNN) models were established to further eliminate interference wavelengths and improve the model effect. The nineteen wavelengths in long-NIR region (1100–2498 nm) with a separation degree greater than 0 were used to establish single-wavelength kNN models, the total recognition-accuracy rates in prediction (RARP) all reached 100%, and the total recognition-accuracy rate in validation (RARV) of the optimal model (1174 nm) reached 97.4%. In the visible (400–780 nm) and short-NIR (780–1100 nm) regions with the separation degree all less than 0, the SDPC-WSP-kNN models were established. The two optimal models (N = 7, 22) were determined, the RARP values reached 100% and 97.4% respectively, and the RARV values reached 96.1% and 94.3% respectively. The results indicated that Vis-NIR spectroscopy combined with few-wavelength kNN has feasibility of high-precision milk powder adulteration discriminant. The few-wavelength schemes provided a valuable reference for designing dedicated miniaturized spectrometer of different spectral regions. The separation degree spectrum and SDPC can be used to improve the performance of spectral discriminant analysis. The SDPC method based on the separation degree priority proposed is a novel and effective wavelength selection method. It only needs to calculate the distance between two types of spectral sets at each wavelength with low computational complexity and good performance. In addition to combining with kNN, SDPC can also be combined with other classifier algorithms (e.g. PLS-DA, PCA-LDA) to expand the application scope of the method.

Assessment of machine-learning methods for the prediction of STN using multi-source data in Fuzhou city, China

This study evaluated the performance of machine-learning approaches to predict Soil Total Nitrogen (STN) using remote sensing and environmental data in the coastal city of Fuzhou, Fujian Province, China. Multisource environmental data was combined to identify important variables for topsoil STN distribution prediction. Additionally, STN content was assessed based on environmental covariates. The results from this study showed that random forest (RF), support vector machine (SVM), artificial neural network (ANN), multi-linear regression (MLR), and locally weighted regression (LWR) can achieve high R2 values of 0.96, 0.92, 0.80, 0.97, and 0.93 with respective RMSECV values of 0.08, 0.35, 0.37, 0.43, and 0.65, respectively. Random Forest (RF) was the most effective model among these methods, with the corrosponding highest R2 and lowest RMSECV. RF and SVM models were used to select important predictors; accordingly, RF selected mainly vegetation indexes while SVM selected Visible-Near-Infrared (VIS-NIR) spectra of the soil. Additionally, STN contents had relationships with most environmental covariates derived from remote sensing, soil spectra, and topographic variables. Spectral transformations improved the correlations with STN where the second derivative and standard normal variate transformations produced the best results. This study suggests that machine-learning methods are practical approaches for the prediction of STN and can be used in similar complex coastal environments.

Robustness of calibration model for prediction of lignin content in different batches of snow pears based on NIR spectroscopy.

Snow pear is very popular in southwest China thanks to its fruit texture and potential medicinal value. Lignin content (LC) plays a direct and negative role (higher concentration and larger size of stone cells lead to thicker pulp and deterioration of the taste) in determining the fruit texture of snow pears as well as consumer purchasing decisions of fresh pears. In this study, we assessed the robustness of a calibration model for predicting LC in different batches of snow pears using a portable near-infrared (NIR) spectrometer, with the range of 1033-2300 nm. The average NIR spectra at nine different measurement positions of snow pear samples purchased at four different periods (batch A, B, C and D) were collected. We developed a standard normal variate transformation (SNV)-genetic algorithm (GA) -the partial least square regression (PLSR) model (master model A) - to predict LC in batch A of snow pear samples based on 80 selected effective wavelengths, with a higher correlation coefficient of prediction set (Rp) of 0.854 and a lower root mean square error of prediction set (RMSEP) of 0.624, which we used as the prediction model to detect LC in three other batches of snow pear samples. The performance of detecting the LC of batch B, C, and D samples by the master model A directly was poor, with lower Rp and higher RMSEP. The independent semi-supervision free parameter model enhancement (SS-FPME) method and the sequential SS-FPME method were used and compared to update master model A to predict the LC of snow pears. For the batch B samples, the predictive ability of the updated model (Ind-model AB) was improved, with an Rp of 0.837 and an RMSEP of 0.614. For the batch C samples, the performance of the Seq-model ABC was improved greatly, with an Rp of 0.952 and an RMSEP of 0.383. For the batch D samples, the performance of the Seq-model ABCD was also improved, with an Rp of 0.831 and an RMSEP of 0.309. Therefore, the updated model based on supervision and learning of new batch samples by the sequential SS-FPME method could improve the robustness and migration ability of the model used to detect the LC of snow pears and provide technical support for the development and practical application of portable detection device.

Congruence Concept for Comparison of Spectra.

This paper introduces an alternative, easy-to-implement spectrum comparison concept. The evaluation procedure is illustrated by artificial and attenuated total reflection Fourier transform infrared (ATR FT-IR) spectra, which it can also be extended to other spectrometries (e.g., ultraviolet-visible or UV-Vis and Raman). The evaluation for the comparison of two spectra is divided into four phases: (i) spectrum pre-treatment (e.g., smoothing and background correction), (ii) standard normal variate (SNV) transformation, (iii) regression analysis of SNV spectra, and (iv) calculation of the quantification index (FG). The FG is derived from the formula of R2. It characterizes and quantifies the identity and/or similarity of the compared spectra.

Comparison of NIR and Raman spectrometries as quantitative methods to monitor polyethylene content in recycled polypropylene

Recycled polypropylene (rPP) often contains a small amount of polyethylene (PE), which frequently compromises the performance of the material and needs to be monitored. In the current work, near-infrared (NIR) and Raman spectrometries were investigated to establish convenient and accurate methods to analyze PE content in rPP. Various spectrum pretreatment methods, including multivariate scattering correction (MSC), standard normal variate transformation (SNV), baseline correction, normalization, smoothing, and first derivative, were tested to reduce noise and improve spectrum quality for partial least squares (PLS) regression. Forward and backward interval partial least squares (FiPLS/BiPLS) methods were employed to optimize spectral range selection. The best NIR model had an R2 of 0.9971 and a root-mean-square error of prediction (RMSEP) of 0.2761 PE wt% in independent validation, and the best Raman model achieved an R2 of 0.9945 and an RMSEP of 0.3818 wt%. The models were further validated with rPP samples. The best Raman model obtained an R2 of 0.9954 and an RMSEP of 0.2404 wt% on non-colored rPP, but it failed to be applied to colored rPP. The best NIR model had a slightly weaker performance on non-colored rPP (R2: 0.9246; RMSEP: 0.9710 wt%), but it was successfully applied to grey commercial rPP samples. This work would help quality assurance of recycled PP materials.

Evaluation of Aspergillus flavus Growth and Detection of Aflatoxin B1 Content on Maize Agar Culture Medium Using Vis/NIR Hyperspectral Imaging

The physiological and biochemical processes of Aspergillus flavus (A. flavus) are complex. Monitoring the metabolic evolution of products during the growth of A. flavus is critical to the overall understanding of the fungal and aflatoxin production detection mechanism. The dynamic growth process of A. flavus and the aflatoxin B1 (AFB1) accumulation in culture media was investigated with a visible/near-infrared hyperspectral imaging (Vis/NIR HSI) system in the range of 400 to 1000 nm. First, the growth of A. flavus and the synthesis pattern of AFB1 were monitored on maize agar medium (MAM) culture for 120 h with a 24-h time-lapse imaging interval. Second, to classify the A. flavus growth, a principal component analysis (PCA) was employed, and a support vector machine (SVM) model was established with the PC1–PC3 as inputs. The results suggested that the PCA-SVM method could distinguish the A. flavus growth time with a classification accuracy larger than 0.97, 0.91, and 0.92 for calibration, validation, and cross-validation, respectively. Third, regression models to predict the AFB1 accumulation using hyperspectral images were developed by comparing different pre-processing methods and key wavelengths. The successive projection algorithm (SPA) was adopted to distill the key wavelengths. The experimental results indicated that the standard normal variate transformation (SNV) with the partial least squares regression (PLSR) achieved the optimal regression performance with an RC value of 0.98–0.99 for calibration and RV values of 0.95–0.96 for validation. Finally, a spatial map of the AFB1 concentration was created using the PLSR model. The spatial regularity of the AFB1 concentration was comparable to the measurement performed. The study proved the potential of the Vis/NIR HSI to characterize the A. flavus growth and the concentration of AFB1 on the MAM over time.

Identification and classification of PAH in asphalt binders with FTIR spectroscopy and multivariate analysis methods

Reclaimed asphalt pavements (RAP) with increased contents of polycyclic aromatic hydrocarbons (PAH) are restricted for reuse due to their harmful effects on humans and the environment. This work investigated whether Fourier transform infrared (FTIR) spectroscopy is suitable for a fast and simple identification and quantification of PAH in binders recovered from reclaimed asphalts. For this, the binders from 34 RAP samples were recovered using a rapid procedure developed at the Universität Kassel and were examined with infrared spectroscopy. The obtained spectra were pre-processed (Standard Normal Variate transformation, 1st derivative) and evaluated using a combination of factor analysis and linear discriminant analysis. The results showed that various PAH groups with differently pronounced aromatic structures are present in the binders. However, with FTIR spectroscopy combined with the multivariate methods, a statistical model was developed allowing for the differentiation between the PAH groups and also for the distinction between PAH contents below or above the threshold of 25mg/kg valid in Germany.

Selecting near-infrared reflection spectroscopy pretreatment methods by chemical components valid and invalid absorption wavebands

An appropriate pretreatment method is crucial to establish a reliable model for near-infrared spectroscopy. It is usually selected by comparing model performance with different pretreatment methods, such as the root mean square error of internal cross-validation or the root mean square error of prediction. When the statistical indexes of the models are similar and indistinguishable, or the results of model calibration and cross-validation are too different, indicating an overfitting situation, it may be due to the selection of an inappropriate pretreatment. In this paper, an approach is proposed to select appropriate pretreatment methods by chemical components valid and invalid absorption wavebands. Due to there being no chemical groups absorption in wavebands of 1250–1350 nm and 1780–1850 nm, they are considered chemical invalid absorption bands. The root mean square distance in the invalid wavebands characterizes the difference in samples’ physical state, such as particle size and instrument error. Except for the invalid absorption band, other wavebands in the near-infrared region are considered chemical valid absorption wavebands. The root mean square distance in the valid absorption wavebands mainly characterize the chemical composition differences between different samples. So, we define effective information rate of spectra. The larger effective information rate of spectra, the more favorable it is to establish a quantitative model. In this study, spectra of 60 wheat powder samples were used to compare seven pretreatment methods. Moreover, the average effective information rates of spectra were calculated for the seven pretreatment methods respectively. The results show that the combination of standard normal variate transformation and first derivative with Savitzky–Golay smoothing is the best. Finally, a comparison of model prediction with different pretreatment methods was made. The results are the same as the former. Therefore, using the effective information rate of spectra for selecting an appropriate near-infrared spectroscopy pretreatment method is practical and can be used not only for near-infrared modeling but also for designing near-infrared instruments as an index for optimization.