Optical Path Length Estimation Research Articles

Development of a Non-Destructive Method for Detection of the Juiciness of Pear via VIS/NIR Spectroscopy Combined with Chemometric Methods.

Juiciness is a primary index of pear quality and freshness, which is also considered as important as sweetness for the consumers. Development of a non-destructive detection method for pear juiciness is meaningful for producers and sellers. In this study, visible−near-infrared (VIS/NIR) spectroscopy combined with different spectral preprocessing methods, including normalization (NOR), first derivative (FD), detrend (DET), standard normal variate (SNV), multiplicative scatter correction (MSC), probabilistic quotient normalization (PQN), modified optical path length estimation and correction (OPLECm), linear regression correction combined with spectral ratio (LRC-SR) and orthogonal spatial projection combined with spectral ratio (OPS-SR), was used for comparison in detection of pear juiciness. Partial least squares (PLS) regression was used to establish the calibration models between the preprocessing spectra (650–1100 nm) and juiciness measured by the texture analyzer. In addition, competitive adaptive reweighted sampling (CARS) was used to identify the characteristic wavelengths and simplify the PLS models. All obtained models were evaluated via Monte Carlo cross-validation (MCCV) and external validation. The PLS model established by 19 characteristic variables after LRC-SR preprocessing displayed the best prediction performance with external verification determination coefficient (R2v) of 0.93 and root mean square error (RMSEv) of 0.97%. The results demonstrate that VIS/NIR coupled with LRC-SR method can be a suitable strategy for the quick assessment of juiciness for pears.

Comparative assessment on smart pre-processing methods for extracting information in FT-NIR measured data

Fourier transform near infrared (FT-NIR) spectroscopy is a rapid and straightforward technology to analyse soil properties. However, FT-NIR measurement of soil is always accompanied with latent noises coming from masking and overlaps because of systematic bias and manual errors. Several pre-processing methods were investigated as smart pre-modelling modes for extracting data information and getting rid of the noises. Using least squares support vector regression (LSSVR), the FT-NIR calibration models were established for predicting target nutrients (nitrogen, phosphorus and potassium). With the combined investigation on noise-sensing pre-processing methods, the LSSVR models were automatically optimized by tuning its kernel parameters. Results show that the pre-processing methods were optimized in smart ways of polynomial modifications or function derivatives on quasi-continuous data. The effective pre-processing methods for the three target nutrients were originated from optical path length estimation and correction (OPLEC) and Whittaker smoother (WTK), switched to modification of the regulation polynomials and 1st- or 2nd- order derivatives, respectively. Thus we concluded that the modified OPLEC and the derivative WTK had strong potentials to replace the traditional pre-processing methods in FT-NIR analysis of soil nutrients.

Parametric-scaling optimization of pretreatment methods for the determination of trace/quasi-trace elements based on near infrared spectroscopy.

This work proposes a parametric-scaling strategy to optimize the pretreatments of near infrared (NIR) spectroscopic data, so as to cope with the difficulty of NIR technology in detecting trace or quasi-trace elements. This novel strategy helps enhancing the signal to noise ratio and contributes to extracting features from the raw spectrum, so that the information corresponding to the trace elements could be detected much easier. However, due to the complexity of NIR data, it is difficult to comprehensively evaluate and compare the performance of different pretreatment methods, especially when multiple target components are determined simultaneously. For this reason, we create some comprehensive model indicators to define the goodness of pretreatments in simultaneous multiple detection of trace elements. In this paper two near infrared data sets have been investigated, one is used to determinate the key indices in the primary screening of thalassemia and the other one is used to detect the heavy metal pollutants in farmland soil. Results show that the proposed parametric-scaling optimization strategy can improve the effect of pretreatments in the determination of trace/quasi-trace elements, and the model performance with the optimized pretreated data is significantly superior to that with the raw data. The optimized Savitzky-Golay smoother (SGS) keeps its merits in the real data examples. Especially, the newly emerged methods optical path length estimation and correction (OPLEC) and Whittaker smoother (WTK), as well as their parametric-scaling modified methods, show their advantages in the comparison with other pretreatments. According to the results of our experiments, they have shown promising potential in the NIR rapid analysis of trace/quasi-trace elements in the field of biomedical science and agricultural science. This is expected to be tested for other analytes with larger variation.

A Comparative Investigation of the Combined Effects of Pre-Processing, Wavelength Selection, and Regression Methods on Near-Infrared Calibration Model Performance.

Near-infrared (NIR) spectroscopy is being widely used in various fields ranging from pharmaceutics to the food industry for analyzing chemical and physical properties of the substances concerned. Its advantages over other analytical techniques include available physical interpretation of spectral data, nondestructive nature and high speed of measurements, and little or no need for sample preparation. The successful application of NIR spectroscopy relies on three main aspects: pre-processing of spectral data to eliminate nonlinear variations due to temperature, light scattering effects and many others, selection of those wavelengths that contribute useful information, and identification of suitable calibration models using linear/nonlinear regression . Several methods have been developed for each of these three aspects and many comparative studies of different methods exist for an individual aspect or some combinations. However, there is still a lack of comparative studies for the interactions among these three aspects, which can shed light on what role each aspect plays in the calibration and how to combine various methods of each aspect together to obtain the best calibration model. This paper aims to provide such a comparative study based on four benchmark data sets using three typical pre-processing methods, namely, orthogonal signal correction (OSC), extended multiplicative signal correction (EMSC) and optical path-length estimation and correction (OPLEC); two existing wavelength selection methods, namely, stepwise forward selection (SFS) and genetic algorithm optimization combined with partial least squares regression for spectral data (GAPLSSP); four popular regression methods, namely, partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), least squares support vector machine (LS-SVM), and Gaussian process regression (GPR). The comparative study indicates that, in general, pre-processing of spectral data can play a significant role in the calibration while wavelength selection plays a marginal role and the combination of certain pre-processing, wavelength selection, and nonlinear regression methods can achieve superior performance over traditional linear regression-based calibration.

Rapid Detection of Surface Color of Shatian Pomelo Using Vis-NIR Spectrometry for the Identification of Maturity

Shatian pomelo is one kind of high-quality fruits grown in south China. Modern agricultural planting technology for Shatian pomelo closely relates to the rapid identification of its maturity. As surface color reflects the maturity of fruits, quantitative determination on the color indices of L*, a*, and b* is significant for maturity judgment. Chemoinformatic models were applied for quantitative detection of L*, a*, and b* for Shatian pomelo, by using visible and near-infrared (Vis-NIR) spectrometry. To enhance the analytical accuracy and model stability, we proposed the combined use of moving window partial least squares (MWPLS) and modified optical path length estimation and correction (MOPLEC) to search for high-signal wavelength combinations, in fully optimizing the modeling parameters. Results indicated that the chemoinformatics that we proposed performed well for nondestructive evaluations of the maturity of Shatian pomelo. This technology is expected to be utilized for quality evaluations of some other agricultural and planting fruits.

An optimization strategy for waveband selection in FT-NIR quantitative analysis of corn protein

An optimization strategy for waveband selection of corn protein was developed based on Fourier transform near infrared (FT-NIR) spectrometry. The optimized moving window partial least squares (OMWPLS) framework was proposed based on different sample partitions for modeling stability. A global-optimal model and some local-optimal models were selected by OMWPLS screening through the full scanning range. The modified optical path length estimation and correction (OPLECm) technique was utilized for further data preprocessing of the OMWPLS-selected wavebands. We finally acquired an optimal and stable model, of which the root mean square error and the correlation coefficients of prediction were 0.413 (%) and 0.939, respectively, and the modeling waveband was 5158–4857 cm−1 with 40 wavenumbers. This selected waveband achieved high accuracy in validation. Moreover, many alternative wavebands with acceptable predictive results were also found. This finding seems valuable and quite practical for the design of a corn-specific NIR instrument. The waveband selection framework confirms the feasibility that FT-NIR quantitative analysis of corn protein can be determined. FT-NIR spectrometry combined with waveband optimization is expected to be an alternative technology for detection of corn chemical components, which is essential for the implementation of chemometric methods in the analysis of corn quality.

Combination of Modified Optical Path Length Estimation and Correction and Moving Window Partial Least Squares to Waveband Selection for the Fourier Transform Near-Infrared Determination of Pectin in Shaddock Peel

The combination of modified optical path length estimation and correction (OPLECm) and moving window partial least squares (MWPLS) was applied to the waveband selection for the Fourier transform near-infrared (FT-NIR) determination of pectin in shaddock peel samples. For MWPLS modeling, the changeable parameter of waveband width was set as the 21 piece-window size selected by the piece-wise multiplicative scatter correction (PMSC) method. Further, OPLECm was implemented for multiplicative scattering correction, and the MWPLS models were optimized by comparing the predictive results of three different cases using OPLECm pretreatment. The optimum case was PLS modeling based on the waveband selected by a moving window and successively pretreated by OPLECm (MW + OPLECm + PLS). The selected waveband was 8573–7784 cm−1 with a width of 410. The optimal number of PLS components was 11, and the corresponding RMSEP, the RP, and RRMSEP were 0.4176 wt%, 0.9185, and 8.43%, respectively. This model was better than other models for MW + OPLECm + PLS. The optimally selected waveband also showed an appreciative result in the validation part. This indicates that MWPLS modeling combined with OPLECm provides an appreciable improvement in the FT-NIR quantitative analysis of pectin in shaddock peel. In addition, a method was proposed for the classification of calibration samples and the prediction samples based on the principles of vector normalization, and all the calibration models and the prediction results were obtained on this classification.

Multivariate Calibration of Near Infrared Spectroscopy in the Presence of Light Scattering Effect: A Comparative Study

When analyzing heterogeneous samples using spectroscopy, the light scattering effect introduces non-linearity into the measurements and deteriorates the prediction accuracy of conventional linear models. This paper compares the prediction performance of two categories of chemometric methods: pre-processing techniques to remove the non-linearity and non-linear calibration techniques to directly model the non-linearity. A rigorous statistical procedure is adopted to ensure reliable comparison. The results suggest that optical path length estimation and correction (OPLEC) and Gaussian process (GP) regression are the most promising among the investigated methods. Furthermore, the combination of pre-processing and non-linear models is explored with limited success being achieved.

PAT: THE EXTRACTION OF MAXIMUM INFORMATION FROM MESSY SPECTRAL DATA

With the increasing take-up of PAT 1 by the pharma- and bio- industries there is a critical need for robust spectral calibrations for processes which are subject to the variations in physical properties such as sample compactness, surface topology, etc. The variation in the optical path-length materializing from the physical differences between samples may result in multiplicative light scattering influencing spectra in a nonlinear manner leading to the poor calibration performance. A new approach “Optical Path Length Estimation and Correction” overcomes the limitations of existing light scattering correction methods.

Extracting Chemical Information from Spectral Data with Multiplicative Light Scattering Effects by Optical Path-Length Estimation and Correction

When analyzing complex mixtures that exhibit sample-to-sample variability using spectroscopic instrumentation, the variation in the optical path length, resulting from the physical variations inherent within the individual samples, will result in significant multiplicative light scattering perturbations. Although a number of algorithms have been proposed to address the effect of multiplicative light scattering, each has associated with it a number of underlying assumptions, which necessitates additional information relating to the spectra being attained. This information is difficult to obtain in practice and frequently is not available. Thus, with a view to removing the need for the attainment of additional information, a new algorithm, optical path-length estimation and correction (OPLEC), is proposed. The methodology is applied to two near-infrared transmittance spectral data sets (powder mixture data and wheat kernel data), and the results are compared with the extended multiplicative signal correction (EMSC) and extended inverted signal correction (EISC) algorithms. Within the study, it is concluded that the EMSC algorithm cannot be applied to the wheat kernel data set due to core information for the implementation of the algorithm not being available, while the analysis of the powder mixture data using EISC resulted in incorrect conclusions being drawn and hence a calibration model whose performance was unacceptable. In contrast, OPLEC was observed to effectively mitigate the detrimental effects of physical light scattering and significantly improve the prediction accuracy of the calibration models for the two spectral data sets investigated without any additional information pertaining to the calibration samples being required.