Automatic analyzer for highly polar carboxylic acids based on fluorescence derivatization-liquid chromatography

Chromatographic Methods and Sample Pretreatment Techniques for Aldehydes, Biogenic Amine, and Carboxylic Acids in Food Samples

This review paper critically examines the current state of research concerning the analysis and derivatization of aldehyde, aromatic hydrocarbons and carboxylic acids components in foods and drinks samples, with a specific focus on the application of Chromatographic techniques. These diverse components, as vital contributors to the sensory attributes of food, necessitate accurate and sensitive analytical methods for their identification and quantification, which is crucial for ensuring food safety and compliance with regulatory standards. In this paper, High-Performance Liquid Chromatography (HPLC) and Gas Chromatographic (GC) methods for the separation, identification, and quantification of aldehydes in complex food matrices were reviewed. In addition, the review explores derivatization strategies employed to enhance the detectability and stability of aldehydes during chromatographic analysis. Derivatization methods, when applied judiciously, improve separation efficiency and increase detection sensitivity, thereby ensuring a more accurate and reliable quantification of aldehyde aromatic hydrocarbons and carboxylic acids species in food samples. Furthermore, methodological aspects encompassing sample preparation, chromatographic separation, and derivatization techniques are discussed. Validation was carried out in term of limit of detections are highlighted as crucial elements in achieving accurate quantification of compounds content. The discussion presented by emphasizing the significance of the combined HPLC and GC chromatography methods, along with derivatization strategies, in advancing the analytical capabilities within the realm of food science.

Derivatization of carboxylic groups prior to their LC analysis - A review

Carboxylic acids (CAs) represent a large group of important molecules participating in various biologically significant processes. Analytical study of these compounds is typically performed by liquid chromatography (LC) combined with various types of detection. However, their analysis is often accompanied by a wide variety of problems depending on used separation system or detection method. The dominant ones are: i) poor chromatographic behavior of the CAs in reversed-phase LC; ii) absence of a chromophore (or fluorophore); iii) weak ionization in mass spectrometry (MS). To overcome these problems, targeted chemical modification, and derivatization, come into play. Therefore, derivatization still plays an important and, in many cases, irreplaceable role in sample preparation, and new derivatization methods of CAs are constantly being developed. The most commonly used type of reaction for CAs derivatization is amidation. In recent years, an increased interest in the isotopic labeling derivatization method has been observed. In this review, we comprehensively summarize the possibilities and actual trends in the derivatization of CAs that have been published over the past decade.

Automatic screening of tear meniscus from lacrimal duct obstructions using anterior segment optical coherence tomography images by deep learning

PURPOSE

We assessed the ability of deep learning (DL) models to distinguish between tear meniscus of lacrimal duct obstruction (LDO) patients and normal subjects using anterior segment optical coherence tomography (ASOCT) images.

METHODS

The study included 117 ASOCT images (19 men and 98 women; mean age, 66.6 ± 13.6 years) from 101 LDO patients and 113 ASOCT images (29 men and 84 women; mean age, 38.3 ± 19.9 years) from 71 normal subjects. We trained to construct 9 single and 502 ensemble DL models with 9 different network structures, and calculated the area under the curve (AUC), sensitivity, and specificity to compare the distinguishing abilities of these single and ensemble DL models.

RESULTS

For the highest single DL model (DenseNet169), the AUC, sensitivity, and specificity for distinguishing LDO were 0.778, 64.6%, and 72.1%, respectively. For the highest ensemble DL model (VGG16, ResNet50, DenseNet121, DenseNet169, InceptionResNetV2, InceptionV3, and Xception), the AUC, sensitivity, and specificity for distinguishing LDO were 0.824, 84.8%, and 58.8%, respectively. The heat maps indicated that these DL models placed their focus on the tear meniscus region of the ASOCT images.

CONCLUSION

The combination of DL and ASOCT images could distinguish between tear meniscus of LDO patients and normal subjects with a high level of accuracy. These results suggest that DL might be useful for automatic screening of patients for LDO.

Prediction of age and brachial-ankle pulse-wave velocity using ultra-wide-field pseudo-color images by deep learning

This study examined whether age and brachial-ankle pulse-wave velocity (baPWV) can be predicted with ultra-wide-field pseudo-color (UWPC) images using deep learning (DL). We examined 170 UWPC images of both eyes of 85 participants (40 men and 45 women, mean age: 57.5 ± 20.9 years). Three types of images were included (total, central, and peripheral) and analyzed by k-fold cross-validation (k = 5) using Visual Geometry Group-16. After bias was eliminated using the generalized linear mixed model, the standard regression coefficients (SRCs) between actual age and baPWV and predicted age and baPWV from the UWPC images by the neural network were calculated, and the prediction accuracies of the DL model for age and baPWV were examined. The SRC between actual age and predicted age by the neural network was 0.833 for all images, 0.818 for central images, and 0.649 for peripheral images (all P < 0.001) and between the actual baPWV and the predicted baPWV was 0.390 for total images, 0.419 for central images, and 0.312 for peripheral images (all P < 0.001). These results show the potential prediction capability of DL for age and vascular aging and could be useful for disease prevention and early treatment.

Accuracy of a deep convolutional neural network in the detection of myopic macular diseases using swept-source optical coherence tomography

This study examined and compared outcomes of deep learning (DL) in identifying swept-source optical coherence tomography (OCT) images without myopic macular lesions [i.e., no high myopia (nHM) vs. high myopia (HM)], and OCT images with myopic macular lesions [e.g., myopic choroidal neovascularization (mCNV) and retinoschisis (RS)]. A total of 910 SS-OCT images were included in the study as follows and analyzed by k-fold cross-validation (k = 5) using DL's renowned model, Visual Geometry Group-16: nHM, 146 images; HM, 531 images; mCNV, 122 images; and RS, 111 images (n = 910). The binary classification of OCT images with or without myopic macular lesions; the binary classification of HM images and images with myopic macular lesions (i.e., mCNV and RS images); and the ternary classification of HM, mCNV, and RS images were examined. Additionally, sensitivity, specificity, and the area under the curve (AUC) for the binary classifications as well as the correct answer rate for ternary classification were examined. The classification results of OCT images with or without myopic macular lesions were as follows: AUC, 0.970; sensitivity, 90.6%; specificity, 94.2%. The classification results of HM images and images with myopic macular lesions were as follows: AUC, 1.000; sensitivity, 100.0%; specificity, 100.0%. The correct answer rate in the ternary classification of HM images, mCNV images, and RS images were as follows: HM images, 96.5%; mCNV images, 77.9%; and RS, 67.6% with mean, 88.9%.Using noninvasive, easy-to-obtain swept-source OCT images, the DL model was able to classify OCT images without myopic macular lesions and OCT images with myopic macular lesions such as mCNV and RS with high accuracy. The study results suggest the possibility of conducting highly accurate screening of ocular diseases using artificial intelligence, which may improve the prevention of blindness and reduce workloads for ophthalmologists.