Automatic soft independent modeling for class analogies

Self Cite

Authentication of Cannabis products is important for assuring the quality of manufacturing, with the increasing consumption and regulation. In this report, a two-stage pipeline was developed for high-throughput screening and chemotyping the spectra from two sets of botanical extracts from the Cannabis genus. The first set contains different marijuana samples with higher concentrations of tetrahydrocannabinol (THC). The other set includes samples from hemp, a variety of Cannabis sativa with the THC concentration below 0.3%. The first stage applies the technique of class modeling to determine whether spectra belong to marijuana or hemp and reject novel spectra that may be neither marijuana nor hemp. An automatic soft independent modeling of class analogy (aSIMCA) that self-optimizes the number of principal components and the decision threshold is utilized in the first pipeline process to achieve excellent efficiency and efficacy. Once these spectra are recognized by aSIMCA as marijuana or hemp, they are then routed to the appropriate classifiers in the second stage for chemotyping the spectra, i.e., identifying these spectra into different chemotypes so that the pharmacological properties and cultivars of the spectra can be recognized. Three multivariate classifiers, a fuzzy rule building expert system (FuRES), super partial least-squares-discriminant analysis (sPLS-DA), and support vector machine tree type entropy (SVMtreeH), are employed for chemotyping. The discriminant ability of the pipeline was evaluated with different spectral data sets of these two groups of botanical samples, including proton nuclear magnetic resonance, mass, and ultraviolet spectra. All evaluations gave good results with accuracies greater than 95%, which demonstrated promising application of the pipeline for automated high-throughput screening and chemotyping marijuana and hemp, as well as other botanical products.

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Pipeline for High-Throughput Modeling of Marijuana and Hemp Extracts

Chen

Harrington

2019

Self Cite

“…18−21 RSM takes advantage of a mathematical model to explore the relationship between a response and different factors so that the optimal levels for different factors can be simultaneously determined. 22,23 Previous studies utilized RSM to efficiently optimize the number of principal components and decision threshold for soft independent modeling for class analogies (SIMCA) to furnish an automatic SIMCA (aSIMCA), 2,24 and the results demonstrated that the overall performance of aSIMCA with RSM is comparable with the one that used the grid search. However, RSM has better efficiency and shorter optimization durations.…”

Section: Introductionmentioning

confidence: 99%

“…This approach evaluates responses for all possible combinations of these two factors across their ranges to find the best condition, which is inefficient and prohibitive for large-scale data sets. Response surface modeling (RSM) is a more efficient approach for optimization and has been widely used. − RSM takes advantage of a mathematical model to explore the relationship between a response and different factors so that the optimal levels for different factors can be simultaneously determined. , Previous studies utilized RSM to efficiently optimize the number of principal components and decision threshold for soft independent modeling for class analogies (SIMCA) to furnish an automatic SIMCA (aSIMCA), , and the results demonstrated that the overall performance of aSIMCA with RSM is comparable with the one that used the grid search. However, RSM has better efficiency and shorter optimization durations.…”

Section: Introductionmentioning

confidence: 99%

Self-Optimizing Support Vector Elastic Net

Chen

Harrington

2020

Self Cite

Chemometrics is widely used to solve various quantitative and qualitative problems in analytical chemistry. A selfoptimizing chemometrics method facilitates scientists to exploit the advantages of chemometrics. In this report, a parameter-free support vector elastic net that self-optimizes two key regularization constants, i.e., λ for L2 regularization and t for L1 regularization, is developed and referred to as self-optimizing support vector elastic net (SOSVEN). Response surface modeling (RSM) and bootstrapped Latin partitions (BLPs) are incorporated for the optimization. Responses at a set of design points over the ranges of the two factors are evaluated with an internal BLP validation using a calibration set. A 2−dimensional interpolation with a cubic spline fits a response surface to determine the best condition that gives the best-estimated response. The SOSVEN with RSM had comparable performances with the one tuned by grid search, while the RSM is more efficient. The developed SOSVEN was compared with two parameter-free chemometrics methods, super partial least-squares regression (sPLSR) and super support vector regression (sSVR) for calibration, and sPLS-discriminant analysis (sPLS−DA) and support vector classification (SVC) for classification. For calibration, the SOSVEN with RSM worked equivalently well or better than the other two self-optimizing methods for the evaluations using meat and hemp oil data sets. For classification, a reference wine data set and mass spectra of different marijuana extracts were used. The three classifiers had similar performances to identify the cultivars of wines with nearly 98% of accuracy. The SOSVEN significantly outperformed sPLS-DA and SVC to classify the mass spectra of marijuana extracts with an overall accuracy of 97%. These results demonstrated excellent abilities of SOSVEN for classification and calibration.

“…Recent work with SIMCA uses experimental design processes to optimize the tuning parameters relative to a receiver operator characteristic (ROC) curve 8,32,33 or mean sensitivity and specificity values. 34 In these situations, the optimized SIMCA was tuned to a fixed set of operating parameters losing the advantage of a fusion classification across multiple tuning parameter values as developed in this paper. Specifically, instead of optimizing SIMCA or another classifier to a unique single tuning parameter value(s), classifier diversity can be obtained by using a collection of the classifier based on a window of respective tuning parameter values for each classifier.…”

mentioning

confidence: 99%

Self-Optimized One-Class Classification Using Sum of Ranking Differences Combined with a Receiver Operator Characteristic Curve

Lemos

Kalivas

2020

A significant and common problem in analytical chemistry is determining if a sample belongs to a specific class, e.g., establishing if a food product is genuine or counterfeit or a tissue sample is benign or malignant. This problem is termed one-class classification (class modeling). Problematic with class modeling is determining which one-class classifier to use followed by the challenge of optimizing the chosen classifier (identifying the best tuning parameter value(s)). With spectroscopic data, two other conundrums arise: which data preprocessing method(s) and spectral region(s) to use. Presented in this paper is a hybrid fusion process that can combine nonoptimized classifiers across multiple instruments, preprocessing methods, and measurements. Instead of optimizing classifiers, a window of tuning parameters is used for each classifier. The flexible fusion method of sum of ranking differences (SRD) is applied to combine all assessment values. Defining the best SRD ranking value (threshold) for determining class membership is the one tuning parameter value needed. However, this SRD ranking value is automatically optimized by using a receiver operator characteristic (ROC) curve. The approach is demonstrated on two analytical data sets. The first is a beer authentication sample set measured on five instruments: near-infrared, mid infrared (MIR), ultraviolet, visible, and thermogravimetric. Three different fusion protocols of all five instruments are demonstrated. The second data set is MIR spectra of strawberry puree with two categories: strawberry puree and nonstrawberry puree. Fusing nonoptimized classifiers provides reliable classifications relative to accuracy, sensitivity, and specificity.