Partial Least Squares Regression Calibration Models Research Articles

Quantitative analysis of four PAHs in oily sludge by surface-enhanced Raman spectroscopy (SERS) combined with partial least squares regression (PLS) based on a novel nano-silver-silicon coupling substrate

Polycyclic aromatic hydrocarbons (PAHs) present in oily sludge generated by the petroleum and petrochemical industries have emerged as a prominent concern within the realm of environmental conservation. The precise determination of PAHs holds immense significance in both petroleum geochemistry and environmental protection. In this study, a combination of surface-enhanced Raman spectroscopy (SERS) and solid–liquid extraction was employed for the screening of PAHs in oily sludge. Methanol was utilized as the extraction solvent for PAHs, while nanosilver-silicon coupling substrates were employed for their detection. The SERS spectrum was acquired using a portable Raman spectrometer. The nano silver-silicon coupling substrate exhibits excellent uniformity, with relative standard deviations (RSDs) of Phenanthrene, Fluoranthrene, Fluorene and Naphthalene (Phe, Flt, Flu and Nap) being 2.8 %, 1.08 %, 1.41 %, and 5.44 % respectively. Moreover, the limits of detection (LODs) achieved remarkable values of 0.542 μg/g, 0.342 μg/g, 0.541 μg/g, and 5.132 μg/g. The quantitative analysis of PAHs in oily sludge was investigated using SERS technology combined with partial least squares (PLS). The optimal PLS calibration model was optimized by combining spectral preprocessing methods and using the SiPLS (Synergy interval partial least squares)-VIP (Variable Importance in Projection) hybrid variable selection strategy. The prediction performance of the D1st (First derivative)-WT (Wavelet transform)-SiPLS-VIP-PLS model was deemed satisfactory, as evidenced by high R2P values of 0.9851, 0.9917, and 0.9925 for Phe, Flt, and Fluorene respectively; additionally, the corresponding MREP values were found to be 0.0580, 0.0668, and 0.0669 respectively. However, for Nap analysis, the D1st-WT-PLS model proved to be a better calibration model with an R2P value of 0.9864 and an MREP (Mean relative error of prediction) value of 0.0713. In summary, SERS technology combined with PLS based on different spectral pretreatment methods and mixed variable selection strategies is a promising method for quantitative analysis of PAHs in oily sludge, which will provide new ideas and methods for the quantitative analysis of PAHs in oily sludge.

Rapid analysis of eucalyptus oil adulteration in Moroccan rosemary essential oil via GC-FID and mid-infrared spectroscopy

Essential Oil (EO) extracted from Rosemary is known for its therapeutic, antifungal, stimulant and antibacterial effects. This study aimed to detect and quantify the adulteration of Rosemary essential oil with different percentages of eucalyptus essential oil, using two analytical techniques: gas chromatography with Flame Ionization Detection (GC-FID) and Fourier Transform Mid-infrared spectroscopy (FT-MIR), combined with chemometric tools such as Principal Component Analysis (PCA), Partial Least Squares regression (PLS-R) and support vector regression (SVR). The use of PCA on the results obtained from GC-FID and FT-MIR indicates the possibility of categorizing the data into two distinct groups: adulterated essential oil and non-adulterated essential oil. However, it is noted that GC-FID can only detect adulteration starting from 40%, while spectroscopy is capable of detecting lower percentages of adulteration. The use of PLS-R and SVR calibration models for adulteration quantification demonstrates high performance capabilities for both techniques (GC-FID and FT-MIR), as indicated by high R2 correlation coefficients indicating good fit, with lower root mean square error (RMSE) values demonstrating predictive accuracy. The results suggest that FT-MIR spectroscopy is preferable to GC-FID for the quantification and discrimination of adulterated essential oils. FT-MIR spectroscopy is considered superior to GC-FID due to its non-destructiveness, speed and lack of sample preparation.

Quantitative analysis of low content polymorphic impurities in canagliflozin tablets by PXRD, NIR, ATR-FITR and Raman solid-state analysis techniques combined with stoichiometry

Canagliflozin (CFZ) was a commercially new class of anti-diabetic drug, which had various anhydrate crystal forms and two hydrate crystal forms (Canagliflozin hemihydrate (Hemi-CFZ) and Canagliflozin monohydrate (Mono-CFZ) crystal form). Commercially available CFZ tablets’ active pharmaceutical ingredient (API) was Hemi-CFZ, which was easy conversion to CFZ or Mono-CFZ under the influence of temperature, pressure, humidity and other factors in tablets processing, storage, and transportation, thus affected bioavailability and efficacy of tablets. Therefore, quantitative analysis low content of CFZ and Mono-CFZ in tablets was essential to control tablets’ quality. The main objective of this study was to examine the feasibility of Powder X-ray Diffraction (PXRD), Near Infrared Spectroscopy (NIR), Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) and Raman for quantitative analysis the low content of CFZ or Mono-CFZ in ternary mixtures. PLSR calibration models for low content of CFZ and Mono-CFZ were established by the solid analysis techniques of PXRD, NIR, ATR-FTIR and Raman combined with various pretreatments (such as Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), Savitzky-Golay First Derivative (SG1st), Savitzky-Golay Second Derivative (SG2nd) and Wavelet Transform (WT)), and the correction models were verified. However, compared with PXRD, ATR-FTIR and Raman, NIR due to its water sensitivity was the most suitable for the quantitative analysis low content of CFZ or Mono-CFZ in tablets. Partial Least Squares Regression (PLSR) model for quantitative analysis low content of CFZ in tablets was as follow: Y = 0.0480 + 0.9928 X, R2 = 0.9986, LOD = 0.1596 %, LOQ = 0.4838 %, SG1st + WT pretreated. And that of Mono-CFZ were Y = 0.0050 + 0.9996 X, R2 = 0.9996, LOD = 0.0164 %, LOQ = 0.0498 %, MSC + WT pretreated and Y = 0.0051 + 0.9996 X, R2 = 0.9996, LOD = 0.0167 %, LOQ = 0.0505 %, SNV + WT pretreated, respectively. That can be used for quantitative analysis of impurity crystal content in drug production to ensure drug quality.

Development of fast analytical method for the detection and quantification of Moroccan picholine extra virgin olive oil adulteration using MIR spectroscopy and chemometrics tools

In this study, the adulteration of Moroccan Picholine extra virgin olive oil with Arbequina virgin olive oil was monitored using the Fourier transform mid-infrared (FT-MIR) spectroscopy technique and chemometrics methodologies. To discriminate between olive oil that has been adulterated and unadulterated, principal component analysis (PCA) was utilized for qualitative analysis. We created the best calibration models for quantitative analysis using principal component regression (PCR) and partial least-squares regression (PLS). The first three principal components account for 95% of the overall variability, according to PCA analysis. PCA allows for the classification of the dataset into two groups: adulterated and unadulterated Moroccan Picholine olive oil. The application of the PLS and PCR calibration models for the quantification of adulteration demonstrates high-performance capabilities, as indicated by high values of correlation coefficients R2 greater than 0.999 and 0.995 and lower values of root mean square error (RMSE) less than 0.767 and 2.16 using PLS and PCR, respectively. According to our results, FT-MIR spectroscopy combined with chemometrics approaches can be used successfully as a simple, quick, and non-destructive method for the quantification and discrimination of adulterated olive oil.

Near-infrared spectroscopy-based nondestructive at-line analysis of physicochemical properties of atorvastatin calcium hydrate after grinding

The purpose of this study was to monitor the physicochemical properties of atorvastatin calcium sesquihydrate (ATC) ground products during grinding and to explore the polymorphous formation caused by intermolecular interactions. Changes in physicochemical properties, such as the surface adsorbed and channel water content (AW), lattice water content (BW), crystallinity (XRD%), and apparent solubility (Cs), of the ground products during grinding were evaluated via X-ray diffractometry, thermogravimetry, near-infrared (NIR) spectroscopy, and initial dissolution kinetic methods. Partial least-squares regression calibration models were created using NIR spectroscopy, to predict the physicochemical properties. The molecular behaviors of these models were analyzed using regression vectors. Changes in the physicochemical properties of the ground products during grinding were caused by the transformation of BW into AW by mechanical energy, where crystalline ATC was transformed into an amorphous solid. The Cs of the ground products increased significantly with increasing grinding time. Based on the NIR spectroscopy results, the amorphous transition of the crystals induced during grinding was caused by the cleavage of intermolecular interactions. Furthermore, over longer grinding times, the polymorphic transitions of amorphous solids to highly soluble amorphous solids were caused by the cleavage of intramolecular interactions between ATC and water in the amorphous ATC complex.

Detection of coconut (Cocos nucivera) sugar adulteration in palm (Arenga pinnata Merrill) sugar by Fourier Transform Infrared (FT-IR) Spectroscopy

Palm sugar which is also named brown sugar is powdered sugar produced from palm extract. Due to the high price of palm sugar, its contamination of materials that are cheap or low quality is inevitable. Usually, adulteration detection is done by conventional methods such as HPLC, TLC, or NMR which are time-consuming and require high-priced equipment, thus impractical for routine and large sample analysis. The aim of this research was to detect adulteration in palm sugar using Fourier Transform Infrared (FT-IR) spectroscopy. The samples used in this study were palm sugar as the main ingredient and coconut sugar as the adulterant. Two chemometric methods namely principal component analysis (PCA) and partial least squares regression (PLSR) were used for analysis. The absorbance data were taken at wavenumber 4000-650 cm-1 . Several concentrations of coconut sugar as an adulterant ranging from 0 to 100% were added to palm sugar. A total of 110 spectra of both pure and adulterated palm sugar samples were divided into two groups, i.e. 73 samples for developing calibration model and 37 samples for developing prediction model. The spectral obtained were pre-processed and analyzed using The Unscrambler X version 10.4. a total of six pre-processing methods were used, i.e., Normalization, Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Baseline. Results showed that PCA was able to classify palm sugar based on adulterant concentrations. PLSR calibration model with a coefficient of determination (Rc2 ) of 0.94 and root mean square error of calibration (RMSEC) of 8% was obtained by applying the MSC method. The model was able to predict coconut sugar adulteration in palm sugar with Rp2 of 0.89 and root mean square error of prediction (RMSEP) of 10.68%. The results confirmed the potential of FT-IR spectroscopy for detecting adulteration in palm sugar.

Potential of On-the-Go Gamma-Ray Spectrometry for Estimation and Management of Soil Potassium Site Specifically

High resolution data on plant available potassium (Ka) is crucial to optimize variable rate potassium fertilizer recommendations, and subsequently improve crop growth and yield. A gamma-ray passive spectrometry sensor was evaluated for on-the-go mapping and management of the spatial distribution of Ka over a 8.4 ha field at Huldenberg, Belgium. During the on-the-go measurement, a 5 s sampling interval was used while driving at 3 km/h speed along 10 m parallel transects. Two calibration models to predict Ka across the field were developed and compared: (1) a simple third order polynomial function (3DPF) was established between the sensor reading of the naturally occurring radioactive isotope of potassium (K-40) and laboratory measured Ka and (2) a partial least squares regression (PLSR) model linking gamma-ray spectra and laboratory measured Ka. Although a relatively small number of samples (45 samples) were used for the development of the PLSR calibration model, the cross-validation analysis resulted in a very good performance with a coefficient of determination (R2) of 0.85, a residual prediction deviation (RPD) of 2.67, a root mean square error of cross-validation (RMSECV) of 2.29 (mg/100 g) and a ratio of performance to interquartile distance (RPIQ) of 2.61. This was a much better result that that obtained with the 3DPF model (R2 = 0.69). The spatial distribution of Ka developed based on 3DPF and PLSR methods showed great similarity with the corresponding map developed using the data from the laboratory analysis. The calculated variable rate fertilizer recommendation based on gamma-ray data showed marginal differences in the amount of K2O fertilizer applied, compared to the uniform rate fertilization based on the conventional laboratory chemical soil analyses. The on-the-go measurement of Ka using gamma-ray spectrometry shows high potential, although the technology needs to be evaluated in a larger number of fields.

Development of Fast Analytical Method for the Detection and Quantification of Honey Adulteration Using Vibrational Spectroscopy and Chemometrics Tools.

In this study, the Fourier transform mid-infrared (FT-MIR) spectroscopy technique combined with chemometrics methods was used to monitor adulteration of honey with sugar syrup. Spectral data were recorded from a wavenumber region of 4000–600 cm−1, with a spectral resolution of 4 cm−1. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used for qualitative analysis to discriminate between adulterated and nonadulterated honey. For quantitative analysis, we used partial least-squares regression (PLS-R) and the support vector machine (SVM) to develop optimal calibration models. The use of PCA shows that the first two principal components account for 96% of the total variability. PCA and HCA allow classifying the dataset into two groups: adulterated and unadulterated honey. The use of the PLS-R and SVM-R calibration models for the quantification of adulteration shows high-performance capabilities represented by a high value of correlation coefficients R2 greater than 98% and 95% with lower values of root mean square error (RMSE) less than 1.12 and 1.85 using PLS-R and SVM-R, respectively. Our results indicate that FT-MIR spectroscopy combined with chemometrics techniques can be used successfully as a simple, rapid, and nondestructive method for the quantification and discrimination of adulterated honey.

Prediction of wine sensory properties using mid-infrared spectra of Cabernet Sauvignon and Chardonnay grape berries and wines

The study determined optimal parameters to four preprocessing techniques for mid-infrared (MIR) spectra of wines and grape berry homogenates and tested MIR’s ability to model sensory properties of research Cabernet Sauvignon and Chardonnay wines. Savitsky-Golay (SG) derivative, smoothing points, and polynomial order, and extended multiplicative signal correction (EMSC) polynomial were investigated as preprocessing techniques at 2, 2, 5, and 3 levels, respectively, all in combination. Preprocessed data were analysed with partial least squares regression (PLS) to model the wine sensory data and the regression coefficients of PLS calibration models (R2) were further analysed with multivariate analysis of variance (MANOVA). SG transformations were significant factors from the MANOVA that influenced R2, while EMSC did not. Overall, PLSR models that predicted wine sensory characteristics gave a poor to moderate R2. Consistently predicting wine sensory attributes within a variety and across vintages is challenging, regardless of using grape or wine spectra as predictors.

Near- and mid-infrared determination of some quality parameters of cheese manufactured from the mixture of different milk species.

This study aimed to evaluate the performance of both near-infrared (NIR) diffuse reflectance and mid-infrared-attenuated total reflectance (MIR-ATR) in determining some quality parameters of a commercial white cheese made of unknown ratios of various milk species. For this purpose, 81 commercial Ezine cheese samples, a special ripened cheese produced in Turkey, containing unknown ratios of bovine, caprine, and ovine milk, were used. Reference analyses, including textural properties, protein content, nitrogen fractions, ripening index coefficients, fat, salt, dry matter-moisture, and ash contents as well as pH and titratable acidity levels, were conducted in the samples following the traditional gold standards. For NIR applications, the spectra of both intact cubes and hand-crushed cheese samples were collected, whereas the spectra of only hand-crushed cheese samples were collected for MIR-ATR. PLSR (Partial Least Squares Regression) calibration models were developed for each parameter (n = 61) and then validated using both cross-validation (leave-one-out approach) and an external validation set (n = 20). Overall, PLSR models developed for total protein, fat, salt, dry matter, moisture, and ash content, as well as pH and titratable acidity, yielded satisfactory performance statistics in the complementary use of NIR and MIR spectroscopy. However, PLSR models of the other parameters, including textural properties, nitrogen fractions, and the ripening index, could only separate high and low values and were not able to make accurate quantitative predictions. NIR spectroscopy was found to be more accurate than that of MIR-ATR spectroscopy for almost all the parameters except for pH and titratable acidity, for which MIR-ATR spectroscopy was superior.

A combined data mining approach for on-line prediction of key soil quality indicators by Vis-NIR spectroscopy

Successful modelling of visible and near-infrared (vis-NIR) spectra for on-line prediction of key soil quality indicators is crucial for accurate variable rate applications of farm input resources. The aim of this paper is to optimize modelling of on-line collected spectra for the prediction of soil pH, organic carbon (OC), extractable phosphorous (P) and potassium (K) by means of spiking, combined with clustering and/or extra-weighting. A mobile fiber-type vis-NIR spectrophotometer (CompactSpec from Tec5 Technology, Germany), with spectral range of 305−1700 nm was calibrated using 100 samples collected from five different fields, which were merged with 28 samples collected from a target field. The resulting dataset was subjected to spectral pretreatments followed by k-means clustering and 95 % confidence ellipsoid, resulting in three optimal datasets. Partial least squares regression (PLSR) analyses were carried out on the calibration set (75 % of samples) for four calibration strategies: (i) non-clustered and non-weighted (NCNW), (ii) clustered and non-weighted (CNW), (iii) non-clustered but extra-weighted (NCW), and (iv) clustered and extra-weighted (CW). Results showed that the quality of on-line predictions was the best after clustering combined with extra-weighting. Modelling based with CW significantly improved model prediction accuracy to be very good for pH (ratio of prediction deviation (RPD) = 2.32) and P (RPD = 2.05), and good for OC (RPD = 1.90) and K (RPD = 1.80), whereas results of NCNW (standard calibration approach) were the poorest to be fair for P (RPD = 1.74) and OC (RPD = 1.50), and poor for K (RPD = 1.1) and pH (RPD = 1.39). It can be concluded that optimal sample selection with k-mean clustering when combined with extra-weighting will result in accurate PLSR calibration models for on-line prediction of soil pH, P, OC and K using a multi-field diverse dataset.

Using FTIRS as pre-screening method for detection of microplastic in bulk sediment samples

We present calibration models for the detection of two types of plastic (LDPE, PET) in sediments, developed from analysis of synthetic sediment mixtures and application of Fourier transform infrared spectroscopy (FTIRS) and partial least squares regression (PLSR) modeling. Synthetic sediment mixtures were produced using ground plastic particles mixed with various different sediment matrixes yielding LDPE and PET contents ranging from 0 to 5 wt%. The resulting PLSR calibration models between the FTIRS spectral information and the defined plastic concentration of the synthetic sediment mixtures show strong cross-validated correlations (R2CV = 0.73 and 0.72) as well as low root-mean square errors of cross-validation (RMSECV = 0.72 and 0.61; 14.4% and 12.2% when expressed as % of gradient). Application of the calibration to natural sediments shows that the method can be used to detect the presence of microplastics in sediment. The results are only semi-quantitative and semi-qualitative, and the method is suitable mainly for samples with very high microplastic concentrations (>1%). However the major advantage of this procedure is the time and cost efficiency. For studies with large amounts of samples (e.g. monitoring applications) we recommend this method as a pre-screening tool for selecting samples with plastic content for further analysis.

Rapid prediction of acid detergent fiber content in corn stover based on NIR-spectroscopy technology

Prediction of acid detergent fiber (ADF) content in corn stover depends on precise data and appropriate analytical methods. In this paper, the optimal PLSR-BPNN model was created for rapidly getting ADF content based on the optimal selection of crucial parameters and the combination of partial least squares regression (PLSR) and back propagation neural network (BPNN). Herein, Mahalanobis distance (MD) was proposed as a tool to recognize and remove outliers. Additionally, on the basis of the characteristic bands extracted by correlation coefficient method (CC), principal component analysis (PCA) was performed to select principal components (PCs) to further compress data of bands for obtaining few characteristic wavelengths. It turned out that the performance of PLSR calibration model based on the selected 10 wavelengths was best. The correlation coefficient (R2), root mean square error of prediction (RMSEP), residual predictive deviation (RPD) and relative standard deviation (RSD) of test set successively were 0.9936, 0.3765, 12.5869, and 0.0087. Besides, BPNN was proposed to cut down the nonlinear regression residual of PLSR model. Genetic algorithm (GA) was applied to avoid the problem of local minimum in network. When RMSEP decreased to the minimum value of 0.2181, PLSR-BPNN model was proven to further improve performance and reached for the best level. Finally, the result of external validation shown that the R2, RMSEP, RPD, RSD were 0.9856, 0.4590, 8.3264, 0.0110, respectively, the created model presented the best predictive performance. Hence, the proposed methods combining with NIR-spectroscopy technology can be used to determine ADF content in corn stover.

The potential of near and mid-infrared spectroscopy for rapid quantification of oleuropein, total phenolics, total flavonoids and antioxidant activity in olive tree (Olea europaea) leaves

Natural foods and food-related antioxidants such as phenolic phytochemicals are of great interest due to their preventive properties against oxidative damage. Olive tree leaves contain high quality and amount of phenolic compounds including oleuropein and therefore considered as nutraceutically valuable materials. The composition of olive leaves, its phenolics and antioxidant power are influenced by numerous factors causing great variation among samples. Additionally, traditional analytical methods performed to quantify these parameters in each product entail long and complicated sample preparation procedures, the use of toxic chemicals, skilled labors, instrumentation and sophisticated laboratory conditions. One appealing alternative is the use of infrared spectroscopy since it gives information about the food composition quickly and it is a multi-parametric and environmentally friendly choice. Therefore, we investigated the oleuropein, total phenolic content, total flavonoid content and antioxidant activity levels of 23 common cultivars of olive leaves harvested from Turkey and Italy using traditional reference methods and also developed near and mid-infrared based partial least squares regression (PLSR) models to predict these parameters without the need of sample preparation. Internal validations of the PLSR calibration models were done using full cross-validation and yielded very high correlation coefficients (≥ 0.95) and low errors in predictions (% standard error of cross-validation for parameters were lower than 7.54%). The levels of all the parameters of interest could be successfully predicted using both NIR and MIR instrumentation within seconds. Overall, infrared spectroscopy along with chemometrics exhibited great potential for future olive leave studies.

Cross-polarized VNIR hyperspectral reflectance imaging for non-destructive quality evaluation of dried banana slices, drying process monitoring and control

Banana and its dried products are one of the most common foods consumed all over the world. Quality attributes of dried banana slices are typically evaluated using time-consuming and labor intensive methods which do not allow to evaluate each individual slice. Replacement of the current destructive methods for quality evaluation of dried banana slices by fast, non-destructive methods would provide large added value to the food industry. Therefore, the aim of this study was to monitor the moisture content, texture and color of banana slices during the drying process in a fast and non-destructive way. VNIR hyperspectral reflectance imaging in the 400–1000 nm range was selected for this purpose. Thanks to a cross-polarized configuration the effects of glare or specular reflection on the banana slice surfaces in the hyperspectral diffuse reflectance images was largely reduced. Reference quality attributes for all the slices (moisture content, texture and color (L*, a*, b* values) obtained using conventional destructive methods and colorimeter were predicted from their corresponding average reflectance spectra extracted from the hyperspectral images by means of partial least squares regression (PLSR). The PLSR calibration models were validated on samples which had not been used for model calibration. The results were very good for water content (R2P = 0.97, RMSEP = 0.05 kg water/kg DM), quite good for and b* value (R2P = 0.83, RMSEP = 1.95), and reasonable for texture (R2P = 0.66, RMSEP = 11.8 N), a* value (R2P = 0.53, RMSEP = 1.32) and L* value (R2P = 0.61, RMSEP = 5.92). Subsequently, these calibration models were used to predict those quality attributes at pixel level for the validation slices to visualize the spatial distribution of these quality parameters at different stages during the drying process. The obtained results clearly indicate the potential of cross-polarized hyperspectral reflectance imaging for non-destructive monitoring of the quality attributes of banana slices during drying.

Application of transmission infrared spectroscopy and partial least squares regression to predict immunoglobulin G concentration in dairy and beef cow colostrum.

The objective of this study was to explore the potential of transmission infrared (TIR) spectroscopy in combination with partial least squares regression (PLSR) for quantification of dairy and beef cow colostral immunoglobulin G (IgG) concentration and assessment of colostrum quality. A total of 430 colostrum samples were collected from dairy (n = 235) and beef (n = 195) cows and tested by a radial immunodiffusion (RID) assay and TIR spectroscopy. Colostral IgG concentrations obtained by the RID assay were linked to the preprocessed spectra and divided into combined and prediction data sets. Three PLSR calibration models were built: one for the dairy cow colostrum only, the second for beef cow colostrum only, and the third for the merged dairy and beef cow colostrum. The predictive performance of each model was evaluated separately using the independent prediction data set. The Pearson correlation coefficients between IgG concentrations as determined by the TIR-based assay and the RID assay were 0.84 for dairy cow colostrum, 0.88 for beef cow colostrum, and 0.92 for the merged set of dairy and beef cow colostrum. The average of the differences between colostral IgG concentrations obtained by the RID- and TIR-based assays were -3.5, 2.7, and 1.4 g/L for dairy, beef, and merged colostrum samples, respectively. Further, the average relative error of the colostral IgG predicted by the TIR spectroscopy from the RID assay was 5% for dairy cow, 1.2% for beef cow, and 0.8% for the merged data set. The average intra-assay CV% of the IgG concentration predicted by the TIR-based method were 3.2%, 2.5%, and 6.9% for dairy cow, beef cow, and merged data set, respectively.The utility of TIR method for assessment of colostrum quality was evaluated using the entire data set and showed that TIR spectroscopy accurately identified the quality status of 91% of dairy cow colostrum, 95% of beef cow colostrum, and 89% and 93% of the merged dairy and beef cow colostrum samples, respectively. The results showed that TIR spectroscopy demonstrates potential as a simple, rapid, and cost-efficient method for use as an estimate of IgG concentration in dairy and beef cow colostrum samples and assessment of colostrum quality. The results also showed that merging the dairy and beef cow colostrum sample data sets improved the predictive ability of the TIR spectroscopy.

Nondestructive analysis of packaged grape tomatoes quality using PCA and PLS regression by means of fiber optic spectroscopy during storage

The postharvest quality and shelf life of fruits and vegetables are controlled by maturity due to their cells which are still alive after harvest and continue to their physiological activity. The purpose of this study was nondestructive analysis of the physicochemical quality parameters of grape tomatoes during storage at (20 and 4 °C) for 20 and 40 days in polyvinyl chloride and high density polyethylene (PVC and HDPE) packaging films and without packaging. A miniature fiber-optic spectrometer system working in absorbance mode in the spectral region (360–1040 nm) was evaluated for this purpose. Partial least squares regression (PLSR) was used to estimate total soluble solids (TSS), titratable acidity (TA), and pH of the grape tomatoes. The PLSR calibration model with TSS during 40 days storage inside HDPE, gave the highest coefficient of determination (R2) = 0.91 and root mean squared error of prediction (RMSECV) = 0.042, while the lowest R2 with TA (0.74) at 20 °C inside HDPE packaging. TSS was changed at 4 °C inside packaging from 6.63°Brix to 6.43°Brix (for PVC) and 6.56°Brix to 5.6°Brix (for HDPE), respectively. Weight loss and moisture content at 20 °C in HDPE were changed from 0 to 2.24 and 91.17 to 89.46%, respectively. Results showed that principal component analysis had nondestructive high-performance in the category and separation of the product inside packaging and without packaging, so that products inside the packaging film (HDPE and PVC), mostly were in the PC1 > 0, while the product without packaging were in the PC1 < 0. The results showed that the physicochemical quality characteristics change significantly during storage of grape tomatoes and this is applicable in the field of postharvest.

Feasibility of using visible and near-infrared reflectance spectroscopy to monitor heavy metal contaminants in urban lake sediment

Contamination of urban lake sediments by heavy metals is a major threat to environmental safety and human health due to its high toxicity and persistence. This paper aims to examine the feasibility of using visible and near-infrared reflectance spectroscopy (VNIRS) to rapidly quantify heavy metals (i.e., As, Cd, Cr, Cu, Hg, Ni, Pb and Zn) in urban lake sediments. Lake sediment samples (n=103) were collected from the East Lake in Wuhan, China. Partial least squares regression (PLSR) calibration models were developed for the heavy metals estimation. Genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were compared to test whether spectral feature selection could improve heavy metals predications or not. Correlation analysis was carried out to gain better understanding of the predictive mechanism for the assessment of the heavy metals in sediments using VNIRS. PLSR calibration models showed that Cd, Hg, Ni and Pb had acceptable model prediction (with r2CV values range from 0.32 to 0.40), while model results for As, Cr, Cu and Zn were unsatisfactory (with r2CV values of 0.01–0.06). These different accuracies were likely caused by the different relationships between heavy metals and spectrally-active constitutes (e.g., total organic carbon, TOC). When compared with full-spectrum PLSR models, GA-PLSR and CARS-PLSR models slightly increased the accuracies by remove uninformative spectral variables. Given these undesired practices, this study demonstrated that VNIRS should not be recommended for heavy metals estimation in urban lake sediment. The inherent correlations between heavy metals and spectrally-active constitutes (e.g., TOC) rather than modeling method (e.g., feature selection algorithms) were critical for the limited heavy metals estimation using VNIRS.

Quantitation of Saccharin and Cyclamate in Tabletop Formulations by Portable Raman and NIR Spectrometers in Combination with Partial Least Squares Regression

Rapid, direct, and reagent-free screening tools using vibrational spectroscopy in combination with partial least squares regression (PLSR) were developed for the determination of sodium saccharin and sodium cyclamate in tabletop formulations. The four vibrational spectroscopic instruments employed were a portable Raman spectrometer, a NIR handheld device, and two FT-NIR benchtop spectrometers. Wavenumber ranges and type of spectral pretreatment were optimized for each PLSR calibration model using an independent validation set. Each sweetener model provided reliable predictions (low errors in validation and r 2 above 0.90) for both saccharin and cyclamate samples. Optimized models were tested with four commercially available tabletop formulations in order to simulate the application of the developed models towards routine sweetener analysis. With the exception of one model, the sweetener concentration predictions in commercial tabletop formulations using the portable devices were not significantly different from those based on spectra collected on the benchtop spectrometers. PLSR-predicted mean sweetener concentrations were within 80–120% of the label declared values, while the sweetener without a label declaration had consistent concentrations across the analytical methods used. As shown by the good agreement between spectroscopic and chromatographic analyses, the portable spectrometers offer an alternative to traditional chromatographic methods. To our knowledge, this is the first time portable Raman and handheld NIR devices with PLSR calibration models have been employed to evaluate sweeteners, and these analytical methods hold potential to be used for rapid screening of tabletop formulations for quality assurance and for regulatory labeling verification.

Big data driven outlier detection for soybean straw near infrared spectroscopy

In near infrared spectroscopy (NIRS) analysis, the prediction ability of the model is seriously affected by outliers that may be the result of errors related to the spectral measurements, the chemical analysis, or a combination of both. In this paper, an outlier detection method is described based on the NIRS analysis data of soybean straw. We improved the resampling by half-mean (RHM) method by including a confidence interval (IRHM) and combined the IRHM and Cook’s distance methods (IRHM-COOK) to detect outlier samples in the NIRS data. The confidence interval is an important parameter in the IRHM-COOK method and the optimal confidence intervals for the IRHM and Cook’s distance methods are combined and used as the confidence interval for the IRHM-COOK method. The selection process for the confidence interval is aimed at relative independence between the detection of the spectrum outliers and the chemical outliers. The experimental results show that the IRHM-COOK method is superior to the traditional Mahalanobis distance method, the IRHM method, and the Cook’s distance method using a partial least squares regression (PLS) model. The determination coefficient (R2) of a hemicellulose PLS calibration model increased from 0.4397918 to 0.5333039 and the root mean square error (RMSE) decreased from 0.7926415 to 0.7287254. The PLS models for lignin and cellulose performed better using the IRHM-COOK method than the original model. The results show that the IRHM-COOK method can effectively identify spectrum outliers and chemical outliers for soybean straw biomass. In addition, it is an effective method to handle NIRS analysis data with one type of outlier, which is proven based on an NIRS analysis of starch.