Comparison of partial least squares regression, least squares support vector machines, and Gaussian process regression for a near infrared calibration

This paper investigates the use of least squares support vector machines and Gaussian process regression for multivariate spectroscopic calibration. The performances of these two non-linear regression models are assessed and compared to the traditional linear regression model, partial least squares regression on an agricultural example. The non linear models, least squares support vector machines, and Gaussian process regression, showed enhanced generalization ability, especially in maintaining homogeneous prediction accuracy over the range. The two non-linear models generally have similar prediction performance, but showed different features in some situations, especially when the size of the training set varies. This is due to fundamental differences in fitting criteria between these models

Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration

In this study, we investigate the use of convolutional neural networks (CNN) for near infrared (NIR) calibration. We propose a unified CNN structure that can be used for general multivariate regression purpose. The comparison between the CNN method and the partial least squares regression (PLSR) method was done on three different NIR datasets of spectra and lab reference values. Datasets are from different sources and contain 6998, 1000 and 415 training and 618, 597 and 108 validation samples, respectively. Results indicated that compared to the PLSR models, the CNN models are more accurate and less noisy. The convolutional layer in the CNN model can automatically find the suitable spectral preprocessing filter on the dataset, which significantly saves efforts in training the model

Self-critical Rumination and Associated Metacognitions as Mediators of the Relationship Between Perfectionism and Self-esteem.

Past research has shown that perfectionism, can negatively impact self-esteem. However, the mediating factors that explain this relationship remain unclear. The current study aimed to investigate whether specific cognitive processes, namely, self-critical rumination and associated metacognitions, mediate this relationship. An opportunity sample of 347 participants completed a battery of online questionnaires measuring clinical perfectionism, self-critical rumination, metacognitions about self-critical rumination, self-esteem, and levels of psychological distress. Several hypotheses were tested to examine the associations between the study variables. Following this, a path analysis was used to determine whether the influence of perfectionistic concerns and perfectionistic striving on self-esteem is mediated by positive metacognitions about self-critical rumination, self-critical rumination, and negative metacognitions about self-critical rumination, serially. Positive metacognitions about self-critical rumination, self-critical rumination, and negative metacognitions about self-critical rumination partially mediated the relationship between perfectionistic concerns and self-esteem and fully mediated the relationship between perfectionistic striving and self-esteem. These results point towards possible interventions for those who struggle with low self-esteem due to their perfectionistic tendencies. Further investigations should explore additional factors that help to explain why perfectionism impacts self-esteem levels, whilst also addressing the limitations of this current research

Two-photon coincident-frequency-entanglement via extended phase matching

We demonstrate a new class of frequency-entangled states generated via spontaneous parametric down-conversion under extended phase matching conditions. Biphoton entanglement with coincident signal and idler frequencies is observed over a broad bandwidth in periodically poled KTiOPO$_4$. We demonstrate high visibility in Hong-Ou-Mandel interferometric measurements under pulsed pumping without spectral filtering, which indicates excellent frequency indistinguishability between the down-converted photons. The coincident-frequency entanglement source is useful for quantum information processing and quantum measurement applications.Comment: 4 pages, 3 figures, submitted to PR

Hierarchical mixture of linear regressions for multivariate spectroscopic calibration: An application for NIR calibration

This paper investigates the use of the hierarchical mixture of linear regressions (HMLR) and variational inference for multivariate spectroscopic calibration. The performance of HMLR is compared to the classical methods: partial least squares regression (PLSR), and PLS embedded locally weighted regression (LWR) on three different NIR datasets, including a publicly accessible one. In these tests, HMLR outperformed the other two benchmark methods. Compared to LWR, HMLR is parametric, which makes it interpretable and easy to use. In addition, HMLR provides a novel calibration scheme to build a two-tier PLS regression model automatically. This is especially useful when the investigated constituent covers a large range

Short Communication: The potential of portable near infrared spectroscopy for assuring quality and authenticity in the food chain, using Iberian hams as an example

This communication assesses the use of a portable near infrared (NIR) instrument to measure quantitative (fatty acid profile) properties and qualitative (‘Premium’ and ‘Non-premium’) categories of individual Iberian pork carcasses at the slaughterhouse. Acorn-fed Iberian pigs have more unsaturated fats than pigs fed conventional compound feed. Recent advances in miniaturisation have led to a number of handheld NIR devices being developed, allowing processing decisions to be made earlier, significantly reducing time and costs. The most common methods used for assessing quality and authenticity of Iberian hams are analysis of the fatty acid composition of subcutaneous fat using gas chromatography and DNA analysis. In this study, NIR calibrations for fatty acids and classification as premium or non-premium ham, based on carcass fat measured in situ, were developed using a portable NIR spectrometer. The accuracy of the quantitative equations was evaluated through the standard error of cross validation or standard error of prediction of 0.84 for palmitic acid (C16:0), 0.94 for stearic acid (C18:0), 1.47 for oleic acid (C18:1) and 0.58 for linoleic acid (C18:2). Qualitative calibrations provided acceptable results, with up to 98% of samples (n = 234) correctly classified with probabilities ⩾0.9. Results indicated a portable NIR instrument has the potential to be used to measure quality and authenticity of Iberian pork carcasses

Probabilistic classification models for the in situ authentication of iberian pig carcasses using near infrared spectroscopy

Iberian pig ham is one of several high value European food products that are the subject of significant attempts at fraud because of the high price differences between commercial categories. Iberian pig products are classified by the Spanish regulations into different categories, mainly depending on the feeding regime during the fattening phase and the race involved, being of Premium quality those products obtained from the animals fed with acorns and other natural resources. Most of the previous NIRS studies related to the Iberian pig have involved the use of at-line instruments to predict quantitative quality parameters. This paper explores the use of the NIR spectra (369 for training and 199 for validation) to classify samples according to the categories Premium (animals fed with acorn) and Non Premium (animals fed with compound feeds), using a MicroNIR™ Pro1700 microspectrometer to analyse individual carcasses in situ at the slaughterhouse line. Four discriminant methods were explored: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), Kernel Bayes and Logistic Regression. These are all discriminant methods that naturally produce classification probabilities to quantify the uncertainty of the results. Rules were tuned and methods compared using both classification error rates and a probability scoring rule. LDA gave the best results, attaining an overall accuracy of 93% and providing well-calibrated classification probabilities

Multivariate predictive models for the prediction of fatty acids in the EU high added-value "acorn Iberian pig ham" using a miniature near-infrared spectroscopy instrument.

Acorn Iberian ham (Jamón Ibérico de Bellota) is one of the most expensive luxury foodstuffs produced in Europe, with a highly appreciated smell and flavour. Its recognized high-sensorial quality and health properties are mainly due to the traditional outdoor feeding system (Montanera) of Iberian pigs (IP), which provides high standards of animal welfare. Nowadays, one of the frauds affecting this product is the use of “special compound feeds” to simulate the fat composition of the acorns through the inclusion of sources of oleic acid like the ones found in pigs fed outdoors. The high prices paid for a cured leg of Iberian ham –ranging from hundreds to thousands of euros- leads to many opportunities for mislabelling and fraud. Fatty acid content of the adipose tissue could provide evidence of the feeding system. Gas chromatography (GC) is used at industry level for production control purposes. However, it is costly and time-consuming, and it is only applied to batches of animals rather than individual pigs. The main goal of this study was to use spectra belonging to a portable NIRS instrument (MicroNIR Onsite Lite, Viavi Solutions Inc.) for on–site quantitative (fatty acid content) analysis of individual Iberian pork carcasses at the slaughterhouse. Performance of this portable instrument was compared with an at-line NIRS monochromator. PLS models were built and optimized resulting in standard errors of cross validation ranging from 0.83 to 0.84 for palmitic acid, 0.94 to 0.99 for stearic acid, 1.47 to 1.56 for oleic acid and 0.53 to 0.58 for linoleic acid

Optimisation of the predictive ability of NIR models to estimate nutritional parameters in elephant grass through LOCAL algorithms

Elephant grass is a tropical forage widely used for livestock feed. The analytical techniques traditionally used for its nutritional evaluation are costly and time consuming. Alternatively, Near Infrared Spectroscopy (NIRS) technology has been used as a rapid analysis technique. However, in crops with high variability due to genetic improvement, predictive models quickly lose accuracy and must be recalibrated. The use of non-linear models such as LOCAL calibrations could mitigate these issues, although a number of parameters need to be optimized to obtain accurate results. The objective of this work was to compare the predictive results obtained with global NIRS calibrations and with LOCAL calibrations, paying special attention to the configuration parameters of the models. The results obtained showed that the prediction errors with the LOCAL models were between 1.6 and 17.5 % lower. The best results were obtained in most cases with a low number of selected samples (n = 100–250) and a high number of PLS terms (n = 20). This configuration allows a reduced computation time with high accuracy, becoming a valuable alternative for analytical determinations that require ruminal fluid, which would improve the welfare of the animals by avoiding the need to surgically prepare animals to estimate the nutritional value of the feeds

Understanding the defect chemistry of alkali metal strontium silicate solid solutions: Insights from experiment and theory

Recent reports of remarkably high oxide ion conduction in a new family of strontium silicates have been challenged. It has recently been demonstrated that, in the nominally potassium substituted strontium germanium silicate material, the dominant charge carrier was not the oxygen ion, and furthermore that the material was not single phase (R. D. Bayliss et. al., Energy Environ. Sci., 2014, DOI: 10.1039/ c4ee00734d). In this work we re-investigate the sodium-doped strontium silicate material that was reported to exhibit the highest oxide ion conductivity in the solid solution, nominally Sr0.55Na0.45SiO2.775. The results show lower levels of total conductivity than previously reported and sub-micron elemental mapping demonstrates, in a similar manner to that reported for the Sr0.8K0.2Si0.5Ge0.5O2.9 composition, an inhomogeneous chemical distribution correlating with a multiphase material. It is also shown that the conductivity is not related to protonic mobility. A density functional theory computational approach provides a theoretical justification for these new results, related to the high energetic costs associated with oxygen vacancy formation