Partial Least Squares Factors Research Articles

Exploring the use of portable infrared spectroscopy as a quality assurance tool for the Australian Native/Traditional food industry: a case study on finger lime (Citrus australasica)

SummaryFinger limes (Citrus australasica) are an Australian native and traditional food that are known for their caviar‐like pearls often referred to as “Citrus Caviar”. This study aimed to spectrally characterise finger lime varieties sourced across South‐East Queensland and Northern New South Wales. The varieties investigated demonstrated a wide diversity in their phenotypic appearance; however, their infrared spectra were similar. The NIR spectra of the samples were collected using a portable instrument and the data analysed using principal component analysis (PCA) and partial least squares (PLS) regression. Differences in the PCA score plot were observed associated with the different varieties analysed. The cross‐validation statistics obtained for the prediction of adulteration of finger with Tahitian lime were R2CV and SECV 0.96% and 7%, respectively (using 8 PLS factors), using PLS regression. This study demonstrated that a portable handheld NIR instrument is able to identify finger lime varieties using both whole fruits and the corresponding peel samples. This study also showed that NIR was able to identify the level of adulteration of finger lime with Tahitian lime.

Prediction of glucose and sucrose values of black tea samples using NIR spectroscopy and chemometrics

FT-NIR and chemometrics are considered one of the most important analytical tools for the quality control of tea. The present study focused on developing and validating a rapid quantification method for the quantification of sugar, specifically sucrose and glucose in black tea using FT-NIR spectroscopy. Calibration models were established by partial least squares (PLS) regression analysis. Fructose, glucose, and sucrose concentrations in black tea were analyzed using high-performance liquid chromatography. The spectral range and the number of PLS factors were optimized for the Residual Predictive Deviation (RPD) and correlation coefficient (r2) of determination. The NIR spectral region between 5878 and 3996 cm−1 (1701–2500 nm) was selected as the best range for predicting glucose, sucrose, and fructose concentrations in black tea. The calibration model developed for sucrose has high RPD (4.29) and r2 (0.95) indicating the potential for predicting the inherent sucrose content in black tea with high accuracy. The calibration model developed for glucose has RPD of 2.73 and r2 (0.87) showing the potential for predicting glucose content in black tea with reasonable accuracy. Both calibration models could be useful for quality assurance purposes in black tea, and easier and faster than the wet chemical HPLC method. FT-NIR spectroscopy coupled with chemometrics has been demonstrated for the first time to quantify the inherent glucose and sucrose concentrations in black tea. Calibration models could be used to determine adulteration of black tea with sucrose and glucose.

Assessing Polymorphic Purity of Rifampicin in Double and Triple-Drug Fixed-Dose Combination Products

First-line tuberculostatic agents, Rifampicin (RIF), Isoniazid (ISH), Ethambutol (ETB), and Pyrazinamide (PZA) are generally administered as a fixed-dose combination (FDC) for improving patient adherence. The major quality challenge of these FDC products is their variable bioavailability, where RIF and its solid state are key factors. In this work, the analysis of the impact of the polymorphism in the performance of RIF in RIF-ISH and PZA-RIF-ISH combined products was carried out by an overall approach that included the development and validation of two methodologies combining near-infrared (NIR) spectroscopy and partial least squares (PLS) to the further evaluation of commercial products.For NIR-PLS methods, training and validation sets were prepared with mixtures of Form I/Form II of RIF, and the appropriate amount of ISH (for double associations) or ISH-PZA (for triple associations). The corresponding matrix of the excipients was added to the mixture of APIs to simulate the environment of each FDC product. Four PLS factors, reduced spectral range, and the combination of standard normal variate and Savitzky-Golay 1st derivative (SNV-D’) were selected as optimum data pre-treatment for both methods, yielding satisfactory recoveries during the analysis of validation sets (98.5±2.0%, and 98.7±1.8% for double- and triple-FDC products, respectively). The NIR-PLS model for RIF-ISH successfully estimated the polymorphic purity of Form II in double-FDC capsules (1.02 ± 0.02w/w). On the other hand, the NIR-PLS model for RIF-ISH-PZA detected a low purity of Form II in triple FDC tablets (0.800 ± 0.021w/w), these results were confirmed by X-ray powder diffraction. Nevertheless, the triple-FDC tablets showed good performance in the dissolution test (Q=99-102%), implying a Form II purity about of 80% is not low enough to affect the safety and efficacy of the product.

Calibration of Proximate Content of Arabica Sidikalang Green Bean Coffee Using NIRS and PLS

Generally, coffee bean’s content measurement is conducted destructively, where it is known to be less effective. Measurement using the Near Infrared Spectroscopy (NIRS) method could be an alternative to a rapid determination of the chemical content non-destructively. The study aimed to obtain a partial least square (PLS) calibration model of water, lipid and carbohydrate content in Sidikalang Arabica green bean coffee. This method can predict the chemical content assisted with several data pretreatments, namely multiple scatter correction (MSC), standard normal variative (SNV), normalization, first derivative (dg1), second derivative (dg2), combination of dg 1 + MSC and combination of dg2 + MSC. Results showed that the best calibration model for water content was obtained using normalization with 6 PLS factor with r value of 0.574. moreover, the best calibration model in lipid content was obtained using MSC with 5 PLS factor with r value of 0.647, and the best calibration model for carbohydrate was using MSC with 6 PLS factors with r value of 0.563. The model obtained from this study is categorized into a moderate model and could be use in simulations with predictive values ​​with moderate accuracy. Generally, coffee bean’s content measurement is conducted destructively, where it is known to be less effective. Measurement using the Near Infrared Spectroscopy (NIRS) method could be an alternative to a rapid determination of the chemical content non-destructively. The study aimed to obtain a partial least square (PLS) calibration model of water, lipid and carbohydrate content in Sidikalang Arabica green bean coffee. This method can predict the chemical content assisted with several data pretreatments, namely multiple scatter correction (MSC), standard normal variative (SNV), normalization, first derivative (dg1), second derivative (dg2), combination of dg 1 + MSC and combination of dg2 + MSC. Results showed that the best calibration model for water content was obtained using normalization with 6 PLS factor with r value of 0.574. moreover, the best calibration model in lipid content was obtained using MSC with 5 PLS factor with r value of 0.647, and the best calibration model for carbohydrate was using MSC with 6 PLS factors with r value of 0.563. The model obtained from this study is categorized into a moderate model and could be use in simulations with predictive values ​​with moderate accuracy.

Determination of Chemical Content of Vanilla Pods (Vanilla planifolia) Non-destructively Using NIR Spectroscopy

This study aims to assess Near infrared (NIR) spectroscopy to predict vanillin and moisture content non-destructively. Twenty-four samples of vanilla pods (@55 grams/sample) from Sumatra and Sulawesi Island were tested. The reflectance spectra of vanilla pod were measured using a NIRFlex N-500 spectrometer at the wavelength of 1000-2500 nm. After that, the vanillin and moisture content were measured using the reference method. The reflectance spectra were transformed to absorbance spectra and several pre-treatment methods of NIR spectra were applied, and the results were calibrated with chemical data using principal component regression (PCR) and partial least square (PLS) methods. The best estimation for vanillin content using absorbance spectra with OSC pre-treatment at 3 PLS factors with the accuracy parameters of r = 0.91, SEC = 1.31%, SEP = 1.34%, CV = 45.60%, RPD = 2.30 with a consistency of 97.96%. The best prediction of vanillin content using PCR method was obtained with SNV pre-treated spectra at 6 PCR factors with the accuracy parameters of r = 0.89, SEC = 1.47%, SEP = 1.49%, CV = 43.21%, RPD = 2.09 and 101.40% consistency. The best estimation for water content using the PLS method is using the SNV pre-treatment spectra with a PLS factor of 5 with the accuracy parameters of r = 0.99, SEC = 2.11%, SEP = 2.13%, CV = 6.12%, RPD = 7.98 with consistency = 98.86%. While using the PCR method, the best estimation for moisture content was also obtained by SNV pre-treated spectra at 4 PCR factors with the accuracy values of r = 0.99, SEC = 2.33%, SEP = 2.25%, CV = 6.25%, RPD = 7.58 and consistency = 103.90%. The PLS calibration provide a better accuracy than the PCR. The NIR spectroscopy associated with the selected pre-treatments and factor numbers of PLS and PCR can be used for determination of chemical content of vanilla pods.

Determination of Total Carotene and Vitamin C in Chili Powder (Capsicum annuum L.) Non-destructively Using Near-Infrared Spectroscopy

Chili (Capsicum annuum L.) is an important source of total carotene and vitamin C. Both substances are widely used in food processing materials, supporting a healthy immune system and medicine. However, destructive testing often obtains information about the substances, which damages the tested material and requires a relatively long analysis. Therefore, this research aims to develop calibration models of total carotene and vitamin C in chili powder for non-destructive testing using near-infrared spectroscopy. The samples consist of four groups of color, i.e., light green, dark green, red tinge, and red, with a total of 84 samples. Seventy percent of the sample was used for calibration, while the rest of the sample was used for validation. Spectra were measured using the NIRFlex N-500 instrument at a wavelength of 1000 nm to 2500 nm and analyzed with the partial least square (PLS) method using three spectral pre-treatments, which are multiplicative scatter correction (MSC), first derivative savitzky-golay, and de-trending. The accuracy and model reliability was determined by the coefficient of determination (R2) and the residual predictive deviation (RPD). The best calibration models were successfully obtained when the spectrum was processed using the first derivative savitzky-golay pre-treatment with 6 and 5 PLS factors for vitamin C and total carotene, respectively. Both models were accurate and can be potentially used for determining the total carotene and vitamin C in chili powder samples non-destructively.

Pelvic Injury Discriminative Model Based on Data Mining Algorithm.

To reduce the dimension of characteristic information extracted from pelvic CT images by using principal component analysis (PCA) and partial least squares (PLS) methods. To establish a support vector machine (SVM) classification and identification model to identify if there is pelvic injury by the reduced dimension data and evaluate the feasibility of its application. Eighty percent of 146 normal and injured pelvic CT images were randomly selected as training set for model fitting, and the remaining 20% was used as testing set to verify the accuracy of the test, respectively. Through CT image input, preprocessing, feature extraction, feature information dimension reduction, feature selection, parameter selection, model establishment and model comparison, a discriminative model of pelvic injury was established. The PLS dimension reduction method was better than the PCA method and the SVM model was better than the naive Bayesian classifier (NBC) model. The accuracy of the modeling set, leave-one-out cross validation and testing set of the SVM classification model based on 12 PLS factors was 100%, 100% and 93.33%, respectively. In the evaluation of pelvic injury, the pelvic injury data mining model based on CT images reaches high accuracy, which lays a foundation for automatic and rapid identification of pelvic injuries.

Non-destructive measurement of antioxidant activity and water content in chili powder (Capsicum annuum L.) using near-infrared spectroscopy

Chili (Capsicum annuum L.) is a potential source of antioxidants due to its high vitamin and lutein contents. The information on antioxidant activity and water content of chili are needed by the chili-based industries which utilise chili's natural antioxidant content for various purposes in health, food, or herbal products. These information are often obtained through destructive testing which damages the tested materials, and also requires a relatively long analysis time. Therefore, the present work aimed to non-destructively measure the antioxidant activity and water content of chili powder using near-infrared spectroscopy (NIS) combined with partial least square (PLS) analysis. A total of 84 samples of chili powder from four different colour groups were used in the present work. Approximately 70% of the samples were used for calibration, while the rest were used for validation. Samples' reflectance data were acquired using NIRFlex N500 Fiber Optic Solids Cells at wavelengths of 1000 - 2500 nm. The antioxidants activity and water content were also analysed destructively for comparison/reference data. PLS was then performed on the spectrum and reference using the Unscrambler X version 11.0 software. There were also three pre-treatments used to improve the spectrum, i.e., multiplicative scatter correction, first derivative Savitzky Golay, and de-trending. The best calibration model was successfully obtained when the antioxidant activity spectrum was processed using the de-trending pre-treatment with six PLS factors. The coefficient of determination (R2) and residual predictive deviation (RPD) values for this were 0.83 and 2.60, respectively. Additionally, the best calibration model was successfully obtained when the water content spectrum was processed using the first derivative Savitzky Golay pre-treatment with five PLS factors. The R2 and RPD values for this were 0.96 and 2.25, respectively. Both models were observed to be accurate, and have the potential to be used for measuring the antioxidant activity and water content of chili powder samples.

Gasoline octane number prediction from near-infrared spectroscopy with an ANN-based model

In this study, determination model for research octane numbers (RONs) of gasoline fuels has been developed based on near-infrared (NIR) spectroscopy and artificial neural networks (ANN). Input features were the partial least squares (PLS) factors extracted from the NIR spectra, and their size number was set from 30 to 46 with a step size of 4. 200 gasoline fuels collected from different regions in China were used to validate the prediction model, and their RONs varies from 90 to 100. The optimal prediction result shows that about 94% of residuals between the predicted and experimental RONs distribute within the experimental error range. It demonstrates that the proposed ANN-NIR model has the potential to predict RONs of real gasoline fuels from various regions in China. Additionally, the contributions of the NIR spectra to the PLS factors could be obtained using the partial least squares regression method, and the results show that the selected PLS factors generally contain the C–H bond vibration information, e.g. the first vibrational overtone of C–H bond, the second vibrational overtone of C–H bond, and the vibrational combination of C–H bond.

Multiple Country Approach to Improve the Test-Day Prediction of Dairy Cows' Dry Matter Intake.

Simple SummaryDry matter intake, related to the number of nutrients available to an animal to meet its production and health needs, is crucial for the economic, environmental, and welfare management of dairy herds. Because the equipment required to weigh the ingested food at an individual level is not broadly available, we propose some new ways to approach the actual dry matter consumed by a dairy cow for a given day. To do so, we used regression models using parity (number of lactations), week of lactation, milk yield, milk mid-infrared spectrum, and prediction of bodyweight, fat, protein, lactose, and fatty acids content in milk. We chose these elements to predict individual dry matter intake because they are either easily accessible or routinely provided by regional dairy organizations (often called “dairy herd improvement” associations). We succeeded in producing a model whose dry matter intake predictions were moderately related to the actual values.We predicted dry matter intake of dairy cows using parity, week of lactation, milk yield, milk mid-infrared (MIR) spectrum, and MIR-based predictions of bodyweight, fat, protein, lactose, and fatty acids content in milk. The dataset comprised 10,711 samples of 534 dairy cows with a geographical diversity (Australia, Canada, Denmark, and Ireland). We set up partial least square (PLS) regressions with different constructs and a one-hidden-layer artificial neural network (ANN) using the highest contribution variables. In the ANN, we replaced the spectra with their projections to the 25 first PLS factors explaining 99% of the spectral variability to reduce the model complexity. Cow-independent 10 × 10-fold cross-validation (CV) achieved the best performance with root mean square errors (RMSECV) of 3.27 ± 0.08 kg for the PLS regression and 3.25 ± 0.13 kg for ANN. Although the available data were significantly different, we also performed a country-independent validation (CIV) to measure the models’ performance fairly. We found RMSECIV varying from 3.73 to 6.03 kg for PLS and 3.69 to 5.08 kg for ANN. Ultimately, based on the country-independent validation, we discussed the developed models’ performance with those achieved by the National Research Council’s equation.

An approach for simultaneous monitoring the content of insensitive agent in the double-base oblate spherical propellant by application of near-infrared spectroscope and partial least squares

The content of insensitive agent is an important parameter that has been shown correlated with the combustion characteristic of double-base oblate spherical propellant (DOSP). This work focused on the feasibility of simultaneous monitoring the content of insensitive agent (dibutyl phthalate (DBP) and N, N′‐dimethyl‐N, N′‐diphenylurea (C2)) in DOSP by using near-infrared (NIR) spectroscope coupled with partial least squares (PLS). The optimal spectral intervals for creating models of DBP and C2 corresponded to 5964 cm−1–4212 cm−1 and 6240 cm−1–4380 cm−1, respectively. It had been demonstrated that derivative tools were more suitable for spectral preprocessing as which had the lowest root mean squares error of cross-validation (RMSECV). The best-performance models of DBP and C2 were built under 4 and 7 PLS factors, respectively. The results showed that the determination coefficients of calibration (Rc2) and the root mean squares error of calibration (RMSEC) were 0.9771 and 0.0173 for DBP; 0.984 and 0.0072 for C2, respectively. Besides, the developed models exhibited excellent ability in prediction with the determination coefficients in prediction (RP2) and the root mean squares error in prediction (RMSEP) of 0.9681 and 0.0275 for DBP, and of 0.9554 and 0.0107 for C2, respectively. The residual predictive deviation (RPD) of prediction set were 5.68 and 5.12 for DBP and C2, respectively. The average relative errors of the proposed and reference methods were 0.652% for DBP, and 0.429% for C2, revealing a good correlation between the reference values and predicted values. Therefore, it concluded that the proposed plan has shown to be an attractive means since its efficient and highly accurate which could provide a better option for quality control in the large-scale production of DOSP.

Fast assay of ethyl acetate content of double-base oblate spherical propellant after balling process by near-infrared spectroscope

Ethyl acetate (EAC) is commonly used as a solvent in the balling process of double-base oblate spherical propellant (DOSP) because of its excellent performance in swelling. However, the residual content of EAC in the DOSP has an adverse effect on combustion performance of DOSP. Therefore, it is necessary to fast assay the EAC content of DOSP in the production process for avoiding economic loss caused by unqualified products. This work is to investigate the feasibility of using a Fourier transform near-infrared (NIR) spectroscope coupled with partial least squares (PLS) algorithm to construct a quantitative model, which can rapidly monitor the EAC content of DOSP after balling process. In the NIR spectra, two characteristic peaks were observed in the range of 6096–5464 cm−1 and 4824–4524 cm−1, which were employed as modeling intervals for analyzing the content of EAC in DOSP intermediate samples. According to the minimum value of the root mean square error of cross-validation (RMSECV), first derivative and standard normal variate (SNV) were chosen as original spectral preprocessing method, and the optimum number of PLS factors for EAC calibration model was 6. The quantitative EAC model was developed based on PLS algorithm. The determination coefficients of the calibration (RC2) and prediction (RP2) for the developed model were equal to 0.9786 and 0.9883, respectively. The root means square error of calibration and prediction (RMSEC and RMSEP) were 0.0536 and 0.0412, respectively. The residual predictive deviation (RPD) values of calibration and prediction were 6.83 and 10.7, respectively. Using independent samples as prediction set to test robustness of the developed model, and the average deviation between traditional measured values and NIR predicted values was 0.017%. Therefore, the experimental results indicated that the NIR spectroscope can give a fast and accurate analysis the EAC content of DOSP after the balling process.

A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra

Lactoferrin (LF) is a glycoprotein naturally present in milk. Its content varies throughout lactation, but also with mastitis; therefore it is a potential additional indicator of udder health beyond somatic cell count. Condequently, there is an interest in quantifying this biomolecule routinely. First prediction equations proposed in the literature to predict the content in milk using milk mid-infrared spectrometry were built using partial least square regression (PLSR) due to the limited size of the data set. Thanks to a large data set, the current study aimed to test 4 different machine learning algorithms using a large data set comprising 6,619 records collected across different herds, breeds, and countries. The first algorithm was a PLSR, as used in past investigations. The second and third algorithms used partial least square (PLS) factors combined with a linear and polynomial support vector regression (PLS + SVR). The fourth algorithm also used PLS factors, but included in an artificial neural network with 1 hidden layer (PLS + ANN). The training and validation sets comprised 5,541 and 836 records, respectively. Even if the calibration prediction performances were the best for PLS + polynomial SVR, their validation prediction performances were the worst. The 3 other algorithms had similar validation performances. Indeed, the validation root mean squared error (RMSE) ranged between 162.17 and 166.75 mg/L of milk. However, the lower standard deviation of cross-validation RMSE and the better normality of the residual distribution observed for PLS + ANN suggest that this modeling was more suitable to predict the LF content in milk from milk mid-infrared spectra (R2v = 0.60 and validation RMSE = 162.17 mg/L of milk). This PLS +ANN model was then applied to almost 6 million spectral records. The predicted LF showed the expected relationships with milk yield, somatic cell score, somatic cell count, and stage of lactation. The model tended to underestimate high LF values (higher than 600 mg/L of milk). However, if the prediction threshold was set to 500 mg/L, 82% of samples from the validation having a content of LF higher than 600 mg/L were detected. Future research should aim to increase the number of those extremely high LF records in the calibration set.

Determination of gel time of prepreg in copper clad laminate industry by near infrared spectroscopy

Gel time of prepreg is an important quality determinant in the manufacturing process of copper clad laminate (CCL). Prepreg consists of a glass fiber reinforcement impregnated to a predetermined level with a resin matrix. In this work, near infrared spectroscopy associated with partial least squares (PLS) regression has been applied to analyse the gel time of prepreg samples in the manufacturing process. A total of 250 prepreg samples were randomly divided into a calibration set and a validation prediction set with a ratio of 4:1. The values of root mean square error of leave-one-out cross-validation (RMSECV) and the coefficient of determination (R2) of the calibration model was 2.95 s and 0.92 respectively, with eight PLS factors used. The results of the paired t-test revealed that there was no significant difference between the NIR method and the reference method. The analytical result showed that, NIR spectroscopy was a rapid, nondestructive, and accurate method for real-time prediction of prepreg quality in the CCL manufacturing process.

Improvement of quantitative structure–retention relationship models for chromatographic retention prediction of peptides applying individual local partial least squares models

In Reversed-Phase Liquid Chromatography, Quantitative Structure–Retention Relationship (QSRR) models for retention prediction of peptides can be built, starting from large sets of theoretical molecular descriptors. Good predictive QSRR models can be obtained after selecting the most informative descriptors. Reliable retention prediction may be an aid in the correct identification of proteins/peptides in proteomics and in chromatographic method development. Traditionally, global QSRR models are built, using a calibration set containing a representative range of analytes. In this study, a strategy is presented to build individual local Partial Least Squares (PLS) models for peptides, based on selected local calibration samples, most similar to the specific query peptide to be predicted. Similar local calibration peptides are selected from a possible calibration set. The calibration samples with the lowest Euclidian distances to the query peptide are considered as most similar. Two Euclidian distances are investigated as similarity parameter, (i) in the autoscaled descriptor space and, (ii) in the PLS factor space of the global calibration samples, both after variable selection by the Final Complexity Adapted Models (FCAM) method. The predictive abilities of individual local QSRR PLS models for peptides, developed with both Euclidian distances, are found significantly better than those of two global models, i.e. before and after FCAM variable selection. The predictive abilities of the local models, developed with distances calculated in the PLS factor space, were best.

Applying a mixed methods design to test saturation for qualitative data in health outcomes research.

Saturation, a core concept in qualitative research, suggests when data collection might end. It is reached when no new relevant information emerges with additional interviews. The aim of this research was to explore whether a mixed methods design could contribute to the demonstration of saturation. Firstly, saturation was conceptualized mathematically using set theory. Secondly, a conversion mixed design was conducted: a set of codes derived from qualitative interviews were quantitized and analyzed using partial least squares (PLS) regression to document whether saturation was reached. A qualitative study conducted by other researchers prior to this work (i.e. none of the present authors was involved in this study) was used to test saturation using PLS regression. This illustrative qualitative study aimed to investigate the impact of Clostridium difficile infection (CDI) on nurses’ work in the hospital and the results were published elsewhere (Guillemin et al. 2015). Semi-structured interviews were conducted with 12 nurses. Saturation was characterized by the cumulative percentage of variability accounted for by PLS factors. After 12 interviews, this percentage was 51% which suggests that saturation was achieved at least on main themes. Two main themes identifying similarities in the experience of nurses caring for patients with CDI were identified: Organization/Coordination of the working day and Time-consuming work. Although dependent on the coding of qualitative data, PLS regression of quantitized data from qualitative interviews generated useful information for the determination of saturation.

Improving forecast accuracy of financial vulnerability: PLS factor model approach

We present a factor augmented forecasting model for assessing the financial vulnerability in Korea. Dynamic factor models often extract latent common factors from a large panel of time series data via the method of the principal components (PC). Instead, we employ the partial least squares (PLS) method that estimates target specific common factors, utilizing covariances between predictors and the target variable. Applying PLS to 198 monthly frequency macroeconomic time series variables and the Bank of Korea's Financial Stress Index (KFSTI), our PLS factor augmented forecasting models consistently outperformed the random walk benchmark model in out-of-sample prediction exercises in all forecast horizons we considered. Our models also outperformed the autoregressive benchmark model in short-term forecast horizons. We expect our models would provide useful early warning signs of the emergence of systemic risks in Korea's financial markets.

Prediction of soluble solid content, vitamin C, total acid and firmness in astringent persimmon (Diospyros kaki L.) cv. Rendeu using NIR spectroscopy

Internal content is one of the important quality criteria in fruit. Quantitative analysis is carried out by using a chemical method. This process is destructive, high cost and time consuming, thus not suitable for real-time measurement. The objective of this study was to evaluate the possibility of the use of near-infrared (NIR) spectroscopy as a rapid method of predicting the internal quantitative quality of persimmon fruit such as solid soluble content (SSC), vitamin C, total acid and firmness. The spectra pre-processing and Partial Least Square (PLS) method were utilized to develop a calibration model. The result showed that the best calibration model was SSC with (MSC) multiplicative scatter correction and 17 PLS factors; vitamin C with MSC and 16 PLS factors; total acid with MSC and 12 PLS factors; firmness with n01 (normalization between 0-1) and 12 PLS factors. This study demonstrated the possibility the use of NIR spectroscopy for rapid and non-destructive measurement of SSC and firmness contents in astringent persimmon. The low accuracy in predicting vitamin C and total acid was obtained due to a limitation of those the contents in persimmon.

A calibration transfer optimized single kernel near-infrared spectroscopic method.

Single kernel near-infrared spectroscopy (SKNIRS) could aid in the quality screening of early-generation seeds, to improve the efficiency of seed breeding. However, the application of SKNIRS is limited due to the irregular physical characteristics, the heterogeneous constituent distributions of individual seeds, and the insufficient detection accuracy of the reference method. The reported near-infrared detection results of single seeds are often less accurate than those of dehusked seeds and seed flour. In this paper, a calibration transfer-optimized single kernel near-infrared spectroscopic method is proposed. This method aims to accurately detect the chemical composition of single seeds by using the calibration model of the corresponding dehusked seeds or seed flour. The proposed method was applied to the analysis of the protein content of a single rice kernel. The near-infrared transmission spectra of three forms of rice (single rice kernel (SRK), single brown rice kernel (SBK) and rice flour (RF)) of 201 individual rice seeds and the corresponding protein content values were obtained. By comparing different pretreatment methods and spectral ranges, the spectral range 950-1250 nm, the standard normal variate transformation (SNV) pretreatment, and 9 PLS factors were selected to construct the optimal partial least squares (PLS) regression models. Then, the protein content of single rice kernels were determined through two different methods: (i) the direct method, in which single rice kernels were analyzed using the single rice kernel model directly; and (ii) the proposed method, in which the spectra of single rice kernels were transferred into the spectra of single brown rice kernels and rice flours with a calibration transfer algorithm, spectral space transformation (SST), and were analyzed by the respective calibration models. The external validation coefficient correlation (R) value of the direct method was 0.971, and the R values of the proposed method were 0.962 (SBK) and 0.975 (RF). The root mean square error of prediction (RMSEP) value of the direct method was 0.423, and the RMSEP of the proposed method were 0.480 (SBK) and 0.401 (RF). In addition, the transfer results among the spectra of three forms of rice were compared. By comparison, the results of the proposed method are fairly close to the results of the direct method. The results indicate that the spectra generated from one individual rice seed can be transferred freely among the three forms by means of calibration transfer. The proposed method is a promising way to overcome the challenges associated with analyzing individual seeds and to improve SKNIRS.

Rapid Analysis of Soil Copper Content for the Pearl River Delta Based on Near-Infrared Spectroscopy Combined with SG-PLS

Using near-infrared (NIR) spectroscopy combined with an optimal method for Savitzky-Golay (SG) smoothing and partial least squares (PLS) regression, a rapid analysis method was established for copper content in the beach reclamation soil samples from Pearl River Delta in China. A framework with calibration, prediction and validation was established by considering randomness and stability. The parameters were optimized according to the comprehensive index (SEP+) to produce modeling stability. The validation results show that, based on the SG-PLS model in long-NIR region (1100 - 2498 nm) with first-order derivative, fifth degree polynomial, seven smoothing points and six PLS factors, the corresponding root mean square error (SEP), correlation coefficient of prediction (RP) and average relative error (ARE) were 0.31 mg·kg-1, 0.924 and 4.5%, respectively. The result indicates high prediction accuracy. The relevant parameter selection can also provide a reference for designing small and dedicated spectrometer.