Optimized variable selection and machine learning models for olive oil quality assessment using portable near infrared spectroscopy.

Olive oil is a key component of the Mediterranean diet, rich in antioxidants and beneficial monounsaturated fatty acids. As a result, high-quality olive oil is in great demand, with its price varying depending on its quality. Traditional chemical tests for assessing olive oil quality are expensive and time-consuming. To address these limitations, this study explores the use of near infrared spectroscopy (NIRS) in predicting key quality parameters of olive oil, including acidity, K232, and K270. To this end, a set of 200 olive oil samples was collected from various agricultural regions of Morocco, covering all three quality categories (extra virgin, virgin, and ordinary virgin). The findings of this study have implications for reducing analysis time and costs associated with olive oil quality assessment. To predict olive oil quality parameters, chemical analysis was conducted in accordance with international standards, while the spectra were obtained using a portable NIR spectrometer. Partial least squares regression (PLSR) was employed along with various variable selection algorithms to establish the relationship between wavelengths and chemical data in order to accurately predict the quality parameters. Through this approach, the study aimed to enhance the efficiency and accuracy of olive oil quality assessment. The obtained results show that NIRS combined with machine learning accurately predicted the acidity using iPLS methods for variable selection, it generates a PLSR with coefficients of determination R2 = 0.94, root mean square error RMSE = 0.32 and ratios of standard error of performance to standard deviation RPD = 4.2 for the validation set. Also, the use of variable selection methods improves the quality of the prediction. For K232 and K270 the NIRS shows moderate prediction performance, it gave an R2 between 0.60 and 0.75. Generally, the results showed that it was possible to predict acidity K232, and K270 parameters with excellent to moderate accuracy for the two last parameters. Moreover, it was also possible to distinguish between different quality groups of olive oil using the principal component analysis PCA, and the use of variable selection helps to use the useful wavelength for the prediction olive oil using a portable NIR spectrometer.

Miniaturized Rapid Electrochemical Immunosensor Based on Screen Printed Carbon Electrodes for Mycobacterium tuberculosis Detection.

In 2019, over 21% of an estimated 10 million new tuberculosis (TB) patients were either not diagnosed at all or diagnosed without being reported to public health authorities. It is therefore critical to develop newer and more rapid and effective point-of-care diagnostic tools to combat the global TB epidemic. PCR-based diagnostic methods such as Xpert MTB/RIF are quicker than conventional techniques, but their applicability is restricted by the need for specialized laboratory equipment and the substantial cost of scaling-up in low- and middle-income countries where the burden of TB is high. Meanwhile, loop-mediated isothermal amplification (LAMP) amplifies nucleic acids under isothermal conditions with a high efficiency, helps in the early detection and identification of infectious diseases, and can be performed without the need for sophisticated thermocycling equipment. In the present study, the LAMP assay was integrated with screen-printed carbon electrodes and a commercial potentiostat for real time cyclic voltammetry analysis (named as the LAMP-Electrochemical (EC) assay). The LAMP-EC assay was found to be highly specific to TB-causing bacteria and capable of detecting even a single copy of the Mycobacterium tuberculosis (Mtb) IS6110 DNA sequence. Overall, the LAMP-EC test developed and evaluated in the present study shows promise to become a cost-effective tool for rapid and effective diagnosis of TB.

Predicting soil phosphorus and studying the effect of texture on the prediction accuracy using machine learning combined with near-infrared spectroscopy.

The estimation of soil phosphorus is essential for agricultural activity. The laboratory chemical analysis techniques are expensive and labor-intensive. In the last decade, near-infrared spectroscopy has been become used as an alternative for soil attributes analysis. It is a rapid technique, and inexpensive relatively. However, this technique requires a calibration step using different machine learning and chemometrics tools. This study aims to develop predictive models for total soil phosphorus and extractable phosphorus by the Olson method (P-Olson) using three regression methods, namely partial least squares (PLS), regression support vector machine (RSVM) and backward propagation neural network (BPNN), combined with a proposed variable selection algorithm (PARtest) and a genetic algorithm PLS (GA-PAS). Also, it aims to investigate the effect of the texture on the accuracy of the prediction. The results show that PARtest combined with PBNN outperform the other used algorithms with an R2t = 0.86, RMSEt = 1104 mg kg-1, and RPD = 3.23 for the TP. For P-Olson the RSVM coupled with GA-PLS outperforms all other methods with an R2t = 0.77, RMSEt = 20.09 mg kg-1, and RPD = 1.90. The use of hierarchical ascendant clustering (HAC) helps to reduce the heterogeneity of soil and helps to increase the quality of prediction. The obtained results show that the models for clayey and loamy soils yielded an excellent prediction quality with an R2t = 0.88, RMSEt = 857.33 mg kg-1, and RPD = 4.10 using BPNN with PARtest for TP. Furthermore, an R2 = 0.83 RMSE = 8.30 mg kg-1, RPD = 11.00 3.11using RSVM with GA-PLS for P-Olson. Thus, the texture has a significant effect on the prediction accuracy.

Development and evaluation of an in-house single step loop-mediated isothermal amplification (SS-LAMP) assay for the detection of Mycobacterium tuberculosis complex in sputum samples from Moroccan patients.

BACKGROUND: Tuberculosis (TB) is a major global health problem and remains the leading cause of morbidity and mortality in developing countries. Routinely used TB diagnostic methods, in most endemic areas, are time-consuming, often less-sensitive, expensive and inaccessible to most patients. Therefore, there is an urgent need for the development of early, easy to use and effective diagnosis tools of TB, which can be effectively integrated into resource limited settings, to anticipate the early treatment and limit further spread of the disease. Over the last decade, Loop-mediated isothermal amplification (LAMP) assays have become a powerful tool for rapid diagnosis of infectious diseases because of the simplicity of device requirements. Indeed, LAMP is a simple, quick and cost effective Isothermal Nucleic Acid Amplification diagnostic test (INAAT) that has the potential to be used in TB endemic settings of resource-poor countries. METHODS: In the present study, we have developed a simple and rapid TB molecular diagnostic test using a Single-Step Loop-mediated isothermal DNA amplification (SS-LAMP) method for the detection of Mycobacterium tuberculosis complex (MTBC) strains, with a simplified sample preparation procedure, eliminating DNA extraction prior to LAMP amplification, DNA initial denaturation and enzymatic inactivation steps during the amplification process. To perform our in-house SS-LAMP assay, a set of six specific primers was specifically designed to recognize eight distinct regions on the MTBC species-specific repetitive insertion sequence 6110 (IS6110). The amplification of the targeted DNA was carried out under isothermal conditions at 65 °C within 1 h. Our protocol was firstly optimized using 60 of confirmed MTBC isolates and a recombinant pGEMeasy-IS6110 vector for sensitivity testing. Thereafter, the assay was evaluated on liquefied sputum specimens collected from 157 Moroccan patients suspected of having TB. RESULTS: Our SS-LAMP developed assay was able to detect MTBC DNA directly from liquefied sputum samples without any prior DNA extraction, denaturation nor the final enzymatic inactivation step. When compared to routinely used Löwenstein Jensen (LJ) Culture method, our SS-LAMP assay is rapid and showed specificity and sensitivity of 99.14 % and 82.93 % respectively which are within the international standards. In addition, the limit of detection of our assay was found to be as little as 10 copies of bacterial DNA. CONCLUSION: To our knowledge, this is the first study using a single step LAMP (SS-LAMP) procedure as a rapid, easy to perform and cost effective testing for TB early detection. This innovative assay could be suitable for low-income countries with restricted health equipment facilities.