Reducing Spectral Analyte Prediction Error with Penalties on Interferents

A goal of chemometric multivariate calibration (modeling) is to predict analyte concentration in a sample using spectral data. Multiple types of modeling methods have been used to predict analyte concentration. However, the samples contain interferents that influence the model and if not fully corrected by the model, analyte concentration prediction errors occur. To reduce the prediction errors caused by interferent species in the system, two new methods were designed to incorporate interferent information. One of the methods uses interferent spectra to require the model to be orthoganol to the interferents. The other method uses interferent spectra to form an orthogonal or oblique model to the interferents. The methods are compared to ridge regression and partial least squares using a near infrared data set. Sum of ranking is used to select models. The new methods have better analyte prediction errors and robustness, but more data sets need to be tested to confirm that both new methods are more effective

Classification using Sum of Ranking Differences of Outlier Measures

A useful application in analytical chemistry is classifying unknown samples into classes. Single-class classification is a type of classification approach where only one well-defined class is of interest. Outlier detection is useful for defining class membership for unknown samples, since outlier detection removes samples that are not represented by the sample class space. When using outlier detection, there are two problems: which outlier measure to use and the tuning parameter value for the chosen outlier measure. The proposed technique for single-class classification using outlier measures eliminates these two problems. To avoid selecting any one particular outlier measure, multiple measures are evaluated by using sum of ranking differences (SRD). The method of SRD is used to evaluate multiple outlier measures to obtain a consensus in classifying a sample. In regards to tuning parameters, a parameter window is used to avoid doing more work, such as having a training set of samples to select a tuning parameter. Wavelength selection and fusing spectra from different instrument is used in conjunction with SRD to provide a robust characterization of the class of interest. Presented are results for the new classification approach on spectral food data sets

Multivariate Calibration Domain Adaptation with Unlabeled Data

Multivariate calibration is about modeling the relationship between a substance\u27s chemical profile and its spectrum (here, near-infrared) in order to predict the concentration of new samples with known spectra. However, these new samples are often measured under different conditions than the primary conditions; different instruments, instrument drift, and temperature all affect the measurement conditions. Domain adaptation (DA) methods force the model to ignore these differences in order to generate an accurate model for the new domain (secondary conditions). There are two fundamental DA processes that individual methods can be classified under. One augments a few samples from the secondary domain with chemical reference values (labels) to the primary data and the other augments only secondary spectra (unlabeled data). In this work, we compare two existing labeled DA methods and two existing unlabeled DA methods to two novel labeled methods and a novel unlabeled approach. Since DA methods require selection of hyperparameters, a model selection framework based on model diversity and prediction similarity (MDPS) is applied to the DA methods. Regardless of the DA method, the MDPS process is shown to select models more accurate than the first quartile of all models generated by the DA process in three near-infrared datasets

Harnessing Model Diversity and Prediction Similarity for Selecting Multivariate Calibration Tuning Parameters

Spectral multivariate calibration offers a cost-effective mechanism to obtain sample analyte values of a substance (e.g. protein level). However, calibration requires varying one or more tuning parameters in order to identify the most accurate model. Model selection is particularly difficult for model updating where spectral and reference information in both the original (primary) conditions and new (secondary) conditions are combined in order to better predict new spectra. Secondary situations can be new instruments, temperatures, or other condition affecting the shape and magnitude of the spectra relative to the primary conditions and analyte values. This poster uses model diversity while maintaining similar analyte prediction values to choose a set of acceptable models. The model selection technique is tested across the calibration method partial least squares and four model updating methods: two require a small set of secondary samples with analyte values and two do not require the secondary analyte values (unlabeled data). Results are presented across a variety of datasets and conditions showing that the cosine of the angle between models in combination with model vector 2-norms and prediction differences are key to selecting models

Raman Spectroscopy and Fusion Classification to Identify Plastic Recycables Targeting Microplastics

Identification of plastic type for microplastic particles (size range of 0.001 mm – 5 mm) is vital to understand the sources and consequences of microplastics in the environment. Fourier- transform infrared and Raman spectroscopy are two dominating techniques used to identify microplastics. The most common method to identify microplastics with spectroscopic data is library searching, a process that utilizes search algorithms against digital databases containing spectra of various plastics. Presented in this study is a new method to utilize spectroscopic data called fusion classification. Fusion classification consists of merging multiple non-optimized classification methods (classifiers) to assign samples into categories (classes). The purpose of this study is to demonstrate the applicability of fusion classification to identify microplastics.

Regularization Adaption Processes for Multivariate Calibration Maintenance

In the field of chemometrics, an important issue in multivariate calibration is model updating. Model updating is the adaption process in which a model obtained for a given set of samples and measurement conditions (primary) is updated to predict the analyte in new samples and measurement conditions (secondary). The calibration method partial least squares is applied with two new updating approaches. In one approach, only one updated model is obtained to predict the analyte amount in both primary and secondary conditions. The other approach forms two updated models in which one model is used to predict in primary conditions and second model based on the first model is used to predict in secondary conditions. Both approaches are evaluated with near-infrared spectral datasets. Datasets include spectra of soil, corn, olive oil adulterated with sunflower and pharmaceutical tablets. Fusion process and single merits are used to select models. Model selection methods are evaluated based on prediction errors using selected models

Fine Tuning Model Updating for Multivariate Calibration Maintenance

In the field of chemometrics, an important issue in multivariate calibration is model updating. Model updating is the adaption process in which a model obtained for a given set of samples and measurement conditions (primary) is updated to predict the analyte in new samples and measurement conditions (secondary). Primary and secondary conditions can be different due to variations in the geographical situation, instrumentation, or environment. Model updating can be performed using labeled data sets containing samples with reference analyte values for both conditions. A common approach is performed by sample augmenting the larger primary labeled sample set with a small weighted secondary labeled sample set. In this situation, only one updating model is obtained to predict the analyte amount in both primary and secondary conditions. The proposed new approach is similar to this common approach, but instead of one updated model, two models are formed simultaneously. One model is used to only predict samples from the primary conditions and the second model is based on this primary model but modified relative to the weighted augmented secondary samples. This second model is used to predict samples from the secondary conditions. Both model updating methods require multiple tuning parameters (penalties)

Fusion of Synchronous Fluorescence Spectra with Application to Argan Oil for Adulteration Analysis

When synchronous fluorescence (SyF) spectroscopy is used for quantitative and qualitative analysis, selection of a useful wavelength interval between the excitation and emission wavelengths (Δλ) is needed. Presented is a fusion approach to combine Δλ intervals thereby negating the selection process. This study uses the fusion of SyF spectra to detect adulteration of argan oil by corn oil and quantitative analysis of the corn oil content. The SyF spectra were acquired by varying the excitation wavelength in the region 300-800 nm using Δλ wavelength intervals from 10 to 100 nm in steps of 10 nm producing 10 sets of SyF spectra. For quantitative analysis, two calibration approaches are evaluated with these 10 SyF spectral datasets. Multivariate calibration by partial least squares (PLS) and a univariate calibration process where the SyF spectra are summed over respective SyF spectral ranges, the area under the curve (AUC) method. For adulteration detection and quantitation of the corn oil, prediction errors decrease with fusion compared to individually using the 10 Δλ interval SyF spectral data sets. For this data set, the AUC method generally provides smaller prediction errors than PLS at individual Δλ intervals as well as with fusion of all 10 Δλ intervals

Restoration of Defaced Serial Numbers Using Lock-In Infrared Thermography (Part I)

Infrared thermal imaging is an evolving approach useful in non-destructive evaluation of materials for industrial and research purposes. This study investigates the use of this method in combination with multivariate data analysis as an alternative to chemical etching; a destructive method currently used to recover defaced serial numbers stamped in metal. This process involves several unique aspects, each of which works to overcome some pertinent challenges associated with the recovery of defaced serial numbers. Infrared thermal imaging of metal surfaces provides thermal images sensitive to local differences in thermal conductivity of regions of plastic strain existing below a stamped number. These strains are created from stamping pressures distorting the atomic crystalline structure of the metal and extend to depths beneath the stamped number. These thermal differences are quite small and thus not readily visible from the raw thermal images of an irregular surface created by removing the stamped numbers. As such, further enhancement is usually needed to identify the subtle variations. The multivariate data analysis method, principal component analysis, is used to enhance these subtle variations and aid the recovery of the serial numbers. Multiple similarity measures are utilised to match recovered numbers to several numerical libraries, followed by application of various fusion rules to achieve consensus identification

Stressor- and Corticotropin releasing Factor-induced Reinstatement and Active Stress-related Behavioral Responses are Augmented Following Long-access Cocaine Self-administration by Rats

Rationale Stressful events during periods of drug abstinence likely contribute to relapse in cocaine-dependent individuals. Excessive cocaine use may increase susceptibility to stressor-induced relapse through alterations in brain corticotropin-releasing factor (CRF) responsiveness. Objectives This study examined stressor- and CRF-induced cocaine seeking and other stress-related behaviors in rats with different histories of cocaine self-administration (SA). Materials and methods Rats self-administered cocaine under short-access (ShA; 2 h daily) or long-access (LgA; 6 h daily) conditions for 14 days or were provided access to saline and were tested for reinstatement by a stressor (electric footshock), cocaine or an icv injection of CRF and for behavioral responsiveness on the elevated plus maze, in a novel environment and in the light–dark box after a 14- to 17-day extinction/withdrawal period. Results LgA rats showed escalating patterns of cocaine SA and were more susceptible to reinstatement by cocaine, EFS, or icv CRF than ShA rats. Overall, cocaine SA increased activity in the center field of a novel environment, on the open arms of the elevated plus maze, and in the light compartment of a light–dark box. In most cases, the effects of cocaine SA were dependent on the pattern/amount of cocaine intake with statistically significant differences from saline self-administering controls only observed in LgA rats. Conclusions When examined after several weeks of extinction/ withdrawal, cocaine SA promotes a more active pattern of behavior during times of stress that is associated with a heightened susceptibility to stressor-induced cocaine-seeking behavior and may be the consequence of augmented CRF regulation of addiction-related neurocircuitry