A Rapid Qualitative Screening Method for Isoniazid Tablets Using Handheld NIR Spectrometers in Two Countries.

BACKGROUND: Isoniazid is a leading tuberculosis treating medication. Global supply chains provide essential medicines such as isoniazid to resource-limited areas. Ensuring the safety and efficaciousness of these medicines is essential to public health programs. Handheld spectrometers are becoming increasingly approachable in cost and usability. As supply chains expand, quality compliance screening of essential medications is necessary in site-specific locations. Here, a brand-specific qualitative discrimination analysis of isoniazid is approached by collecting data from two handheld spectrometers in two countries with the intent to build a multi-location quality compliance screening method for a brand of isoniazid. METHODS: Two handheld spectrometers (900-1700 nm) were used to collect spectra from five manufacturing sources (N = 482) in Durham, North Carolina, USA, and Centurion, South Africa. A qualitative brand differentiation method was established from both locations by applying a Mahalanobis distance thresholding method as a measure of assessing similarity. RESULTS: Combining data from both locations resulted in a 100% classification accuracy, at both locations, for brand 'A' and resulted in the four other brands classifying as dissimilar. Bias was found between sensors in terms of resulting Mahalanobis distances, but the classification method proved to be robust enough to accommodate. Several spectral peaks found in isoniazid references appear within the 900-1700 nm range, as well as variation in the excipients per manufacturer. CONCLUSIONS: Results show promise for compliance screening isoniazid as well as other tablets in multiple geographic locations using handheld spectrometers.

Biopolymer encapsulated silver nitrate nanoparticle substrates with surface-enhanced Raman spectroscopy (SERS) for Salmonella detection from chicken rinse.

Salmonella is commonly found on broiler chickens during processing. This study investigates the Salmonella detection method that reduces the necessary time for confirmation, by collecting surface-enhanced Raman spectroscopy (SERS) spectra from bacteria colonies, applied to a substrate of biopolymer encapsulated AgNO3 nanoparticles. Chicken rinses containing Salmonella Typhimurium (ST) were analyzed by SERS and compared to traditional plating and PCR analyses. SERS spectra from confirmed ST and non-Salmonella colonies appear similar in spectra composition, but with different peak intensities. t-Test on the peak intensities showed that ST and non-Salmonella colonies were significantly different (α = 0.0045) at 5 peaks, 692 cm-1, 718 cm-1, 791 cm-1, 859 cm-1, and 1018 cm-1. A support vector machine (SVM) classification algorithm was able to separate ST and non-Salmonella samples with an overall classification accuracy of 96.7 %.

Impact of Smart Snacks Intervention on College Students' Vending Selections.

OBJECTIVES: To place Smart Snacks in vending machines and determine if different sales strategies affect Smart Snack selection. PARTICIPANTS: University students living in resident halls. METHODS: Vending machines included 50% Smart Snacks and 50% non compliant snacks. Three sales strategies targeted student selection of Smart Snacks: Reduced price, signage, and nutrition education activities. Three-way ANOVA was used for analysis. RESULTS: There was a statistically significant three-way interaction on snack selection between sales strategy, study period, and snack type, F(4, 77) = 3.33, P = .01. There were no statistically significant simple two-way interaction between study period and sales strategy for either Smart Snack, F(1, 77) = 1.62, P = 0.18, or NC snack types, F(1, 77) = 2.02, P = 0.07. CONCLUSIONS: Sales strategies did not affect Smart Snack selections. Advocates for healthier snacks in vending machines can align with university administrations to establish nutrient guidelines.

Establishment of instrument operation qualification and routine performance qualification procedures for handheld near-infrared spectrometers used at different locations within a laboratory network.

Quality assurance of finished pharmaceuticals is a necessity in ensuring the safety of consumers. There is a need for low-cost and portable rapid screening methods of pharmaceuticals in resource limited areas. Recent advances in technology have made handheld and low-cost diffuse reflectance spectrometers available to the public. While these handheld spectrometers offer advantages over benchtop spectrometers, the accuracy and repeatability must be assessed before these instruments can be used for quality assurance screening. Here, five handheld spectrometers of the same model were purchased, where an in-house installation qualification and operational qualification (IQOQ) was subsequently established for the instruments. Wavelength and photometric accuracy (and repeatability), spectroscopic noise, stray light, and bandpass were assessed between instruments. Results were found to be consistent between the spectrometers, passing IQOQ procedures, and were determined to be ready for field use. Once the handheld spectrometer's performance was verified, a practical and low-cost daily performance verification was established using common high density polyethylene vial caps on location in South Africa, Thailand, and the United States. A Mahalanobis distance-based classifier found the five spectrometers to be in agreement.

A low-cost and portable near-infrared spectrometer using open-source multivariate data analysis software for rapid discriminatory quality assessment of medroxyprogesterone acetate injectables.

Medroxyprogesterone acetate (MPA) injectable suspensions are used by millions of women for family planning and hormonal therapy. Falsified or substandard medications may result in a health risk for consumers. Near-infrared spectroscopy (NIR) has previously been applied as a means of non-destructive and rapid screening of product quality compliance. These methods offer advantages but can be logistically and cost prohibitive for field use in resource limited areas. Here, a handheld spectrometer (900-1700 nm) with open-sourced software is used to evaluate vials of MPA from three suppliers (N = 227 vials) and verified by a benchtop UV-VIS-NIR (350-2500 nm) with licensed software. Multivariate data analysis assesses the spectral signatures of samples and builds a discriminant classification method based on Mahalanobis distances calculated from a principal component analysis scores. The handheld device paired with open-source software resulted in a product discrimination accuracy of 100% (verified by benchtop UV-VIS-NIR and chemical testing data) as well indicating that the low-cost field portable device is suitable for rapidly assessing samples in resource limited areas for consistency of manufacturing and sourcing.

Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks‡.

Foodborne pathogens have become ongoing threats in the food industry, whereas their rapid detection and classification at an early stage are still challenging. To address early and rapid detection, hyperspectral microscope imaging (HMI) technology combined with convolutional neural networks (CNN) was proposed to classify foodborne bacterial species at the cellular level. HMI technology can simultaneously obtain both spatial and spectral information of different live bacterial cells, while two CNN frameworks, U-Net and one-dimensional CNN (1D-CNN), were employed to accelerate the data analysis process. U-Net was used for automating cellular regions of interest (ROI) segmentation, which generated accurate cell-ROI masks in a shorter timeframe than the conventional Otsu or Watershed methods. The 1D-CNN was employed for classifying the spectral profiles extracted from cell-ROI and resulted in a higher accuracy (90%) than k-nearest neighbor (81%) and support vector machine (81%). Overall, the CNN-assisted HMI technology showed potential for foodborne bacteria detection.

Rapid Identification of Campylobacter Strains Cultured Under Aerobic Incubation Using Hyperspectral Microscope Imaging.

ABSTRACT: Campylobacter is an organism of concern for food safety and is one of the leading causes of foodborne bacterial gastroenteritis. This pathogen can be found in broiler chickens, and the level of allowable contamination of processed poultry is regulated by federal agency guidelines. Traditional methods for detecting and isolating this pathogen from broiler chicken carcasses require time, expensive reagents, and artificially generated microaerophilic atmospheres. An aerobic medium that simplifies the procedure and reduces the expense of culturing Campylobacter has been recently described, and Campylobacter can be grown in this medium in containers that are incubated aerobically. Hyperspectral microscopic imaging (HMI) has been proposed for early and rapid detection of pathogens at the cellular level. The objective of the present study was to utilize HMI to compare differences between Campylobacter cultures grown under artificially produced microaerobic atmospheres and cultures grown in aerobic medium. Hyperspectral microscopic images of three Campylobacter strains were collected cultures grown for 48 h microaerophilically and for 24 and 48 h aerobically, and a quadratic discriminant analysis was used to characterize the bacterial variability. Microaerobically cultured bacteria were detected with 98.7% accuracy, whereas detection accuracy of cultures grown in the novel medium was slightly reduced (-4.8 and -3.2% for 24 and 48 h, respectfully). The Mahalanobis distance multivariate metric was applied to quantify strain variability under all three treatment conditions. Across all strains and treatments, little cluster variation was present (4.22 to 4.42), indicating the consistency of the images collected from the three strains. The classification and spectral consistency was similar for cultures incubated in the aerobic medium for 24 h and cultures grown for 48 h under microaerobic conditions.

Detection of Salmonella from chicken rinsate with visible/near-infrared hyperspectral microscope imaging compared against RT-PCR.

Salmonella is an organism of importance to the poultry industry with increasingly stringent government regulatory standards. Real-time polymerase chain reaction (RT-PCR) and plating procedures on nutrient enriched growth media have been the standard detection methods of Salmonella from broiler chicken carcasses for years. These methods are proven, but offer disadvantages in the amount of time or reoccurring sample cost. Here, we propose the use of a hyperspectral microscope imaging system (HMI) for comparison to standard detection methods. Broiler chicken carcasses were rinsed and plated on Salmonella selective agar. Colonies from plates were picked and RT-PCR was used as a confirmation test to verify plating results, while HMI was collected from the same colonies. Spectral signatures of cells were extracted between 450 and 800 nm from HMI collected with 100x objective. A quadratic discriminant analysis (QDA) was used to classify cells as either Salmonella positive or negative (n = 341). Spectra preprocessing minimized the influence of cellular shape on the spectra, increasing the initial classification accuracy of 81.8-98.5%, yielding a sensitivity of 1.0, and a specificity of 0.963. Results showed the potential as an initial investigation of HMI as a microbial confirmation tool, compared to RT-PCR.

Rapid and early detection of Salmonella serotypes with hyperspectral microscopy and multivariate data analysis.

This study was designed to evaluate hyperspectral microscope images for early and rapid detection of Salmonella serotypes Enteritidis, Heidelberg, Infantis, Kentucky, and Typhimurium at incubation times of 6, 8, 10, 12, and 24 h. Images were collected by an acousto-optical tunable filter hyperspectral microscope imaging system with a metal halide light source measuring 89 contiguous wavelengths every 4 nm between 450 and 800 nm. Pearson correlation values were calculated for incubation times of 8, 10, and 12 h and compared with data for 24 h to evaluate the change in spectral signatures from bacterial cells over time. Regions of interest were analyzed at 30% of the pixels in an average cell size. Spectral data were preprocessed by applying a global data transformation algorithm and then subjected to principal component analysis (PCA). The Mahalanobis distance was calculated from PCA score plots for analyzing serotype cluster separation. Partial least-squares regression was applied for calibration and validation of the model, and soft independent modeling of class analogy was utilized to classify serotype clusters in the training set. Pearson correlation values indicate very similar spectral patterns for reduced incubation times ranging from 0.9869 to 0.9990. PCA score plots indicated cluster separation at all incubation times, with incubation time Mahalanobis distances of 2.146 to 27.071. Partial least-squares regression had a maximum root mean squared error of calibration of 0.0025 and a root mean squared error of validation of 0.0030. Soft independent modeling of class analogy correctly classified values at 8 h (98.32%), 10 h (96.67%), 12 h (88.33%), and 24 h (98.67%) with the optimal number of principal components (four or five). The results of this study suggest that Salmonella serotypes can be classified by applying a PCA to hyperspectral microscope imaging data from samples after only 8 h of incubation.