Universal consistency and robustness of localized support vector machines

Authenticity assessment of ground black pepper by combining headspace gas-chromatography ion mobility spectrometry and machine learning

Currently, the authentication of ground black pepper is a major concern, creating a need for a rapid, highly sensitive and specific detection tool to prevent the introduction of adulterated batches into the food chain. To this aim, head space gas-chromatography ion mobility spectrometry (HS-GC-IMS), combined with machine learning, is tested in this initial, proof-of-concept study. A broad variety of authentic samples originating from eight countries and three continents were collected and spiked with a range of adulterants, both endogenous sub-products and an assortment of exogenous materials. The method is characterized by no sample preparation and requires 20 min for chromatographic separation and ion mobility data acquisition. After an explorative analysis of the data, those were submitted to two different machine learning algorithms (partial least squared discriminant analysis-PLS-DA and support vector machine-SVM). While the PLS-DA model did not provide fully satisfactory performances, the combination of HS-GC-IMS and SVM successfully classified the samples as authentic, exogenously-adulterated or endogenously-adulterated with an overall accuracy of 90 % and 96 % on withheld test set 1 and withheld test set 2, respectively (at a 95 % confidence level). Some limitations, expected to be mitigated by further research, were encountered in the correct classification of endogenously adulterated ground black pepper. Correct categorization of the ground black pepper samples was not adversely affected by the operator or the time span of data collection (the method development and model challenge were carried out by two operators over 6 months of the study, using ground black pepper harvested between 2015 and 2019). Therefore, HS-GC-IMS, coupled to an intelligent tool, is proposed to: (i) aid in industrial decision-making before utilization of a new batch of ground black pepper in the production chain; (ii) reduce the use of time-consuming conventional analyses and; (iii) increase the number of ground black pepper samples analyzed within an industrial quality control frame.

Universal consistency of twin support vector machines

A classification problem aims at constructing a best classifier with the smallest risk. When the sample size approaches infinity, the learning algorithms for a classification problem are characterized by an asymptotical property, i.e., universal consistency. It plays a crucial role in measuring the construction of classification rules. A universal consistent algorithm ensures that the larger the sample size of the algorithm is, the more accurately the distribution of the samples could be reconstructed. Support vector machines (SVMs) are regarded as one of the most important models in binary classification problems. How to effectively extend SVMs to twin support vector machines (TWSVMs) so as to improve performance of classification has gained increasing interest in many research areas recently. Many variants for TWSVMs have been proposed and used in practice. Thus in this paper, we focus on the universal consistency of TWSVMs in a binary classification setting. We first give a general framework for TWSVM classifiers that unifies most of the variants of TWSVMs for binary classification problems. Based on it, we then investigate the universal consistency of TWSVMs. To do this, we give some useful definitions of risk, Bayes risk and universal consistency for TWSVMs. Theoretical results indicate that universal consistency is valid for various TWSVM classifiers under some certain conditions, including covering number, localized covering number and stability. For applications of our general framework, several variants of TWSVMs are considered.