Clinical domain knowledge-derived template improves post hoc AI explanations in pneumothorax classification.

OBJECTIVE: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. Recently, artificial intelligence (AI), especially deep learning (DL), has been increasingly employed for automating the diagnostic process of pneumothorax. To address the opaqueness often associated with DL models, explainable artificial intelligence (XAI) methods have been introduced to outline regions related to pneumothorax. However, these explanations sometimes diverge from actual lesion areas, highlighting the need for further improvement. METHOD: We propose a template-guided approach to incorporate the clinical knowledge of pneumothorax into model explanations generated by XAI methods, thereby enhancing the quality of the explanations. Utilizing one lesion delineation created by radiologists, our approach first generates a template that represents potential areas of pneumothorax occurrence. This template is then superimposed on model explanations to filter out extraneous explanations that fall outside the template's boundaries. To validate its efficacy, we carried out a comparative analysis of three XAI methods (Saliency Map, Grad-CAM, and Integrated Gradients) with and without our template guidance when explaining two DL models (VGG-19 and ResNet-50) in two real-world datasets (SIIM-ACR and ChestX-Det). RESULTS: The proposed approach consistently improved baseline XAI methods across twelve benchmark scenarios built on three XAI methods, two DL models, and two datasets. The average incremental percentages, calculated by the performance improvements over the baseline performance, were 97.8% in Intersection over Union (IoU) and 94.1% in Dice Similarity Coefficient (DSC) when comparing model explanations and ground-truth lesion areas. We further visualized baseline and template-guided model explanations on radiographs to showcase the performance of our approach. CONCLUSIONS: In the context of pneumothorax diagnoses, we proposed a template-guided approach for improving model explanations. Our approach not only aligns model explanations more closely with clinical insights but also exhibits extensibility to other thoracic diseases. We anticipate that our template guidance will forge a novel approach to elucidating AI models by integrating clinical domain expertise.

Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs.

BACKGROUND: Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs has not been well studied. METHODS: We trained, validated, and externally tested a deep-learning system to classify optic disks as being normal or having papilledema or other abnormalities from 15,846 retrospectively collected ocular fundus photographs that had been obtained with pharmacologic pupillary dilation and various digital cameras in persons from multiple ethnic populations. Of these photographs, 14,341 from 19 sites in 11 countries were used for training and validation, and 1505 photographs from 5 other sites were used for external testing. Performance at classifying the optic-disk appearance was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity, as compared with a reference standard of clinical diagnoses by neuro-ophthalmologists. RESULTS: The training and validation data sets from 6779 patients included 14,341 photographs: 9156 of normal disks, 2148 of disks with papilledema, and 3037 of disks with other abnormalities. The percentage classified as being normal ranged across sites from 9.8 to 100%; the percentage classified as having papilledema ranged across sites from zero to 59.5%. In the validation set, the system discriminated disks with papilledema from normal disks and disks with nonpapilledema abnormalities with an AUC of 0.99 (95% confidence interval [CI], 0.98 to 0.99) and normal from abnormal disks with an AUC of 0.99 (95% CI, 0.99 to 0.99). In the external-testing data set of 1505 photographs, the system had an AUC for the detection of papilledema of 0.96 (95% CI, 0.95 to 0.97), a sensitivity of 96.4% (95% CI, 93.9 to 98.3), and a specificity of 84.7% (95% CI, 82.3 to 87.1). CONCLUSIONS: A deep-learning system using fundus photographs with pharmacologically dilated pupils differentiated among optic disks with papilledema, normal disks, and disks with nonpapilledema abnormalities. (Funded by the Singapore National Medical Research Council and the SingHealth Duke-NUS Ophthalmology and Visual Sciences Academic Clinical Program.).

Deep Learning System Outperforms Clinicians in Identifying Optic Disc Abnormalities.

BACKGROUND: The examination of the optic nerve head (optic disc) is mandatory in patients with headache, hypertension, or any neurological symptoms, yet it is rarely or poorly performed in general clinics. We recently developed a brain and optic nerve study with artificial intelligence-deep learning system (BONSAI-DLS) capable of accurately detecting optic disc abnormalities including papilledema (swelling due to elevated intracranial pressure) on digital fundus photographs with a comparable classification performance to expert neuro-ophthalmologists, but its performance compared to first-line clinicians remains unknown. METHODS: In this international, cross-sectional multicenter study, the DLS, trained on 14,341 fundus photographs, was tested on a retrospectively collected convenience sample of 800 photographs (400 normal optic discs, 201 papilledema and 199 other abnormalities) from 454 patients with a robust ground truth diagnosis provided by the referring expert neuro-ophthalmologists. The areas under the receiver-operating-characteristic curves were calculated for the BONSAI-DLS. Error rates, accuracy, sensitivity, and specificity of the algorithm were compared with those of 30 clinicians with or without ophthalmic training (6 general ophthalmologists, 6 optometrists, 6 neurologists, 6 internists, 6 emergency department [ED] physicians) who graded the same testing set of images. RESULTS: With an error rate of 15.3%, the DLS outperformed all clinicians (average error rates 24.4%, 24.8%, 38.2%, 44.8%, 47.9% for general ophthalmologists, optometrists, neurologists, internists and ED physicians, respectively) in the overall classification of optic disc appearance. The DLS displayed significantly higher accuracies than 100%, 86.7% and 93.3% of clinicians (n = 30) for the classification of papilledema, normal, and other disc abnormalities, respectively. CONCLUSIONS: The performance of the BONSAI-DLS to classify optic discs on fundus photographs was superior to that of clinicians with or without ophthalmic training. A trained DLS may offer valuable diagnostic aid to clinicians from various clinical settings for the screening of optic disc abnormalities harboring potentially sight- or life-threatening neurological conditions.

Effects of Sous-Vide on Quality, Structure and Flavor Characteristics of Tilapia Fillets.

To investigate the effects of traditional high-temperature cooking and sous-vide cooking on the quality of tilapia fillets, muscle microstructure, texture, lipid oxidation, protein structure, and volatile compounds were analyzed. In comparison with samples subjected to traditional high-temperature cooking, sous-vide-treated samples exhibited less protein denaturation, a secondary structure dominated by α-helices, a stable and compact structure, a significantly higher moisture content, and fewer gaps in muscle fibers. The hardness of the sous-vide-treated samples was higher than that of control samples, and the extent of lipid oxidation was significantly reduced. The sous-vide cooking technique resulted in notable changes in the composition and relative content of volatile compounds, notably leading to an increase in the presence of 1-octen-3-ol, α-pinene, and dimethyl sulfide, and a decrease in the levels of hexanal, D-limonene, and methanethiol. Sous-vide treatment significantly enhanced the structural stability, hardness, and springiness of muscle fibers in tilapia fillets and reduced nutrient loss, enriched flavor, and mitigated effects on taste and fishy odor.

DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.

PURPOSE: To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs. DESIGN: DeepLensNet was trained by applying deep learning models to the Age-Related Eye Disease Study (AREDS) dataset. PARTICIPANTS: A total of 18 999 photographs (6333 triplets) from longitudinal follow-up of 1137 eyes (576 AREDS participants). METHODS: Deep learning models were trained to detect and quantify nuclear sclerosis (NS; scale 0.9-7.1) from 45-degree slit-lamp photographs and cortical lens opacity (CLO; scale 0%-100%) and posterior subcapsular cataract (PSC; scale 0%-100%) from retroillumination photographs. DeepLensNet performance was compared with that of 14 ophthalmologists and 24 medical students. MAIN OUTCOME MEASURES: Mean squared error (MSE). RESULTS: On the full test set, mean MSE for DeepLensNet was 0.23 (standard deviation [SD], 0.01) for NS, 13.1 (SD, 1.6) for CLO, and 16.6 (SD, 2.4) for PSC. On a subset of the test set (substantially enriched for positive cases of CLO and PSC), for NS, mean MSE for DeepLensNet was 0.23 (SD, 0.02), compared with 0.98 (SD, 0.24; P = 0.000001) for the ophthalmologists and 1.24 (SD, 0.34; P = 0.000005) for the medical students. For CLO, mean MSE was 53.5 (SD, 14.8), compared with 134.9 (SD, 89.9; P = 0.003) for the ophthalmologists and 433.6 (SD, 962.1; P = 0.0007) for the medical students. For PSC, mean MSE was 171.9 (SD, 38.9), compared with 176.8 (SD, 98.0; P = 0.67) for the ophthalmologists and 398.2 (SD, 645.4; P = 0.18) for the medical students. In external validation on the Singapore Malay Eye Study (sampled to reflect the cataract severity distribution in AREDS), the MSE for DeepSeeNet was 1.27 for NS and 25.5 for PSC. CONCLUSIONS: DeepLensNet performed automated and quantitative classification of cataract severity for all 3 types of age-related cataract. For the 2 most common types (NS and CLO), the accuracy was significantly superior to that of ophthalmologists; for the least common type (PSC), it was similar. DeepLensNet may have wide potential applications in both clinical and research domains. In the future, such approaches may increase the accessibility of cataract assessment globally. The code and models are available at https://github.com/ncbi/deeplensnet.

Optic Disc Classification by Deep Learning versus Expert Neuro-Ophthalmologists.

OBJECTIVE: To compare the diagnostic performance of an artificial intelligence deep learning system with that of expert neuro-ophthalmologists in classifying optic disc appearance. METHODS: The deep learning system was previously trained and validated on 14,341 ocular fundus photographs from 19 international centers. The performance of the system was evaluated on 800 new fundus photographs (400 normal optic discs, 201 papilledema [disc edema from elevated intracranial pressure], 199 other optic disc abnormalities) and compared with that of 2 expert neuro-ophthalmologists who independently reviewed the same randomly presented images without clinical information. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated. RESULTS: The system correctly classified 678 of 800 (84.7%) photographs, compared with 675 of 800 (84.4%) for Expert 1 and 641 of 800 (80.1%) for Expert 2. The system yielded areas under the receiver operating characteristic curve of 0.97 (95% confidence interval [CI] = 0.96-0.98), 0.96 (95% CI = 0.94-0.97), and 0.89 (95% CI = 0.87-0.92) for the detection of normal discs, papilledema, and other disc abnormalities, respectively. The accuracy, sensitivity, and specificity of the system's classification of optic discs were similar to or better than the 2 experts. Intergrader agreement at the eye level was 0.71 (95% CI = 0.67-0.76) between Expert 1 and Expert 2, 0.72 (95% CI = 0.68-0.76) between the system and Expert 1, and 0.65 (95% CI = 0.61-0.70) between the system and Expert 2. INTERPRETATION: The performance of this deep learning system at classifying optic disc abnormalities was at least as good as 2 expert neuro-ophthalmologists. Future prospective studies are needed to validate this system as a diagnostic aid in relevant clinical settings. ANN NEUROL 2020;88:785-795.

Interpretation of artificial intelligence studies for the ophthalmologist.

PURPOSE OF REVIEW: The use of artificial intelligence (AI) in ophthalmology has increased dramatically. However, interpretation of these studies can be a daunting prospect for the ophthalmologist without a background in computer or data science. This review aims to share some practical considerations for interpretation of AI studies in ophthalmology. RECENT FINDINGS: It can be easy to get lost in the technical details of studies involving AI. Nevertheless, it is important for clinicians to remember that the fundamental questions in interpreting these studies remain unchanged - What does this study show, and how does this affect my patients? Being guided by familiar principles like study purpose, impact, validity, and generalizability, these studies become more accessible to the ophthalmologist. Although it may not be necessary for nondomain experts to understand the exact AI technical details, we explain some broad concepts in relation to AI technical architecture and dataset management. SUMMARY: The expansion of AI into healthcare and ophthalmology is here to stay. AI systems have made the transition from bench to bedside, and are already being applied to patient care. In this context, 'AI education' is crucial for ophthalmologists to be confident in interpretation and translation of new developments in this field to their own clinical practice.

Correlation between autochthonous microbial communities and key odorants during the fermentation of red pepper (Capsicum annuum L.).

High-throughput sequencing and gas chromatography-mass spectrometry (GC-MS) were used to investigate changes in bacterial and fungal communities and volatile flavor compounds during a 32-day fermentation process of red pepper (Capsicum annuum L.). Key odorants were identified by olfactometry combined with GC-MS. Sixteen volatile compounds differed significantly after fermentation, including seven odorants. After fermentation, 1-butanol, 3-methyl-, acetate, phenol, 4-ethyl-2-methoxy-, octanoic acid, ethyl ester, styrene and 2-methoxy-4-vinylphenol were the key odorants, producing a flavor described as peppery, fruity, sour, and spicy. The correlation between microorganisms and odorants in the fermentation was studied and 18 odorants significantly correlated with the core microbial communities in the fermented samples. For further analysis, strains of seven genera were isolated and correlation analysis by O2PLS indicated that Aspergillus, Bacillus, Brachybacterium, Microbacterium and Staphylococcus were highly correlated with the flavor formation. These findings would help to understand the fermentation mechanism of fermented red pepper flavor formation.

Effect of exogenous γ-aminobutyric acid treatment on the enzymatic browning of fresh-cut potato during storage.

The aim of this study was to evaluate the effect of γ-aminobutyric acid (GABA) treatment on the enzymatic browning of fresh-cut potatoes. The browning index and activities of browning and defense-related enzymes were analyzed after 0, 1, 2, 3, 4, 5, and 6 days of storage at 4 °C. The results showed that the treatment with 20 g/L GABA for 10 min significantly retarded the browning of fresh-cut potatoes. GABA inhibited the browning of fresh-cut potatoes by enhancing the activities of catalase and superoxide dismutase, and decreasing the activities of polyphenol oxidase and reactive oxygen species. The results suggest that GABA plays an important role in reducing the browning of fresh-cut potatoes. Hence, GABA treatment is a promising approach for reducing the browning and maintaining the quality of fresh-cut potatoes.

Research progress of fishy odor in aquatic products: From substance identification, formation mechanism, to elimination pathway.

Fishy odor in aquatic products has a significant impact on the purchasing decisions of consumers. The production of aquatic products is a complex process involving culture, processing, transportation, and storage, which contribute to decreases in flavor and quality. This review systematically summarizes the fishy odor composition, identification methods, generation mechanism, and elimination methods of fishy odor compounds from their origin and formation to their elimination. Fishy odor compounds include aldehydes (hexanal, heptanal, and nonanal), alcohols (1-octen-3-ol), sulfur-containing compounds (dimethyl sulfide), and amines (trimethylamine). The mechanism of action of various factors affecting fishy odor is revealed, including environmental factors, enzymatic reactions, lipid oxidation, protein degradation, and microbial metabolism. Furthermore, the control and removal of fishy odor are briefly summarized and discussed, including masking, elimination, and conversion. This study provides a theoretical basis from source to elimination for achieving targeted regulation of the flavor of aquatic products, promoting industrial innovation and upgrading.

Identification and validation of core microbes for the formation of the characteristic flavor of fermented oysters (Crassostrea gigas).

Self-fermented oyster homogenates were prepared to investigate core microbes and their correlations with flavor formation mechanisms. Five bacterial and four fungal genera were identified. Correlation analysis showed that Saccharomyces cerevisiae, Kazachstania, and L. pentosus were core species for the flavor of fermented products. Four core microbes were selected for inoculation into homogenates. Twelve key aroma compounds with odor activity values >1 were identified by gas chromatography-mass spectrometry. L. plantarum and S. cerevisiae were beneficial for producing key aroma compounds such as 1-octen-3-ol, (E,Z)-2,6-nonadienal, and heptanal. Fermentation with four microbes resulted in significant increases in contents of Asp, Glu, Lys, inosine monophosphate, and guanosine monophosphate, which provided freshness and sweetness. Fermentation with four microbes resulted in high digestibility, antioxidant abilities, and zinc contents. This study has elucidated the mechanism of flavor formation by microbial action and provides a reference for targeted flavor control in fermented oyster products.

Study on antimicrobial activity of sturgeon skin mucus polypeptides (Rational Design, Self-Assembly and Application).

Despite the favorable biocompatibility of natural antimicrobial peptides (AMPs), their scarcity limits their practical application. Through rational design, the activity of AMPs can be enhanced to expand their application. In this study, we selected a natural sturgeon epidermal mucus peptide, AP-16 (APATPAAPALLPLWLL), as the model molecule and studied its conformational regulation and antimicrobial activity through amino acid substitutions and N-terminal lipidation. The structural and morphological transitions of the peptide self-assemblies were investigated using circular dichroism and transmission electron microscopy. Following amino acid substitution, the conformation of AL-16 (AKATKAAKALLKLWLL) did not change. Following N-terminal alkylation, the C8-AL-16 and C12-AL-16 conformations changed from random coil to ß-sheet or α-helix, and the self-assembly changed from nanofibers to nanospheres. AL-16, C8-AL-16, and C8-AL-16 presented significant antimicrobial activity against Pseudomonas and Shewanella at low concentrations. N-terminal alkylation effectively extended the shelf life of Litopenaeus vannamei. These results support the application of natural AMPs.

Contribution of phospholipase B to the formation of characteristic flavor in steamed sturgeon meat.

Sensory analysis and untargeted lipidomics were employed to study the impact of phospholipase B (PLB) on lipid oxidation and flavor in steamed sturgeon meat, revealing the inherent relationship between lipid oxidation and flavor regulation. The research verified that PLB effectively suppresses fat oxidation and improves the overall taste of steamed sturgeon meat. Furthermore, the PLB group identified 52 compounds, and the content of odor substances such as isoamyl alcohol and hexanal was reduced compared with other groups. Finally, lipid substances containing eicosapentaenoic acid (EPA) or docosahexaenoic acid (DHA) were screened out from 32 kinds of differential phospholipids. Through Pearson correlation analysis, it was observed that certain differential phospholipids such as PC (22:6) and PC (22:5) exhibited varying correlations with odor substances like hexanal and isovaleraldehyde. These findings suggest that PLB specifically affects certain phospholipids, leading to the production of distinct volatile substances through oxidative degradation.

Correlation investigation between core microbe inoculation and the evolution of flavor characteristics during the storage of sturgeon caviar (Acipenser gueldenstaedtii).

The volatile and non-volatile compounds were monitored to investigate the microbial evolution associated with the characteristic flavors for sturgeon caviar during refrigeration. The results revealed that the composition of volatile compounds changed significantly with prolonged refrigeration time, especially hexanal, nonanal, phenylacetaldehyde, 3-methyl butyraldehyde, and 1-octen-3-ol. The nonvolatile metabolites were mainly represented by the increase of bitter amino acids (Thr. Ser, Gly, Ala, and Pro) and a decrease in polyunsaturated fatty acids, especially an 18.63 % decrease in 5 months of storage. A total of 332 differential metabolites were mainly involved in the biosynthetic metabolic pathways of α-linolenic acid, linoleic acid, and arachidonic acid. The precursors associated with flavor evolution were mainly phospholipids, including oleic, linoleic, arachidonic, eicosapentaenoic (EPA), and docosahexaenoic (DHA) acids. The most abundant at the genus level was Serratia, followed by Arsenophnus, Rhodococcus, and Pseudomonas, as obtained by high-throughput sequencing. Furthermore, seven core microorganisms were isolated and characterized from refrigerated caviar. Among them, inoculation with Mammalian coccus and Bacillus chrysosporium restored the flavor profile of caviar and enhanced the content of nonvolatile precursors, contributing to the characteristic aroma attributes of sturgeon caviar. The study presents a theoretical basis for the exploitation of technologies for quality stabilization and control of sturgeon caviar during storage.

Optical coherence tomography choroidal enhancement using generative deep learning.

Spectral-domain optical coherence tomography (SDOCT) is the gold standard of imaging the eye in clinics. Penetration depth with such devices is, however, limited and visualization of the choroid, which is essential for diagnosing chorioretinal disease, remains limited. Whereas swept-source OCT (SSOCT) devices allow for visualization of the choroid these instruments are expensive and availability in praxis is limited. We present an artificial intelligence (AI)-based solution to enhance the visualization of the choroid in OCT scans and allow for quantitative measurements of choroidal metrics using generative deep learning (DL). Synthetically enhanced SDOCT B-scans with improved choroidal visibility were generated, leveraging matching images to learn deep anatomical features during the training. Using a single-center tertiary eye care institution cohort comprising a total of 362 SDOCT-SSOCT paired subjects, we trained our model with 150,784 images from 410 healthy, 192 glaucoma, and 133 diabetic retinopathy eyes. An independent external test dataset of 37,376 images from 146 eyes was deployed to assess the authenticity and quality of the synthetically enhanced SDOCT images. Experts' ability to differentiate real versus synthetic images was poor (47.5% accuracy). Measurements of choroidal thickness, area, volume, and vascularity index, from the reference SSOCT and synthetically enhanced SDOCT, showed high Pearson's correlations of 0.97 [95% CI: 0.96-0.98], 0.97 [0.95-0.98], 0.95 [0.92-0.98], and 0.87 [0.83-0.91], with intra-class correlation values of 0.99 [0.98-0.99], 0.98 [0.98-0.99], and 0.95 [0.96-0.98], 0.93 [0.91-0.95], respectively. Thus, our DL generative model successfully generated realistic enhanced SDOCT data that is indistinguishable from SSOCT images providing improved visualization of the choroid. This technology enabled accurate measurements of choroidal metrics previously limited by the imaging depth constraints of SDOCT. The findings open new possibilities for utilizing affordable SDOCT devices in studying the choroid in both healthy and pathological conditions.

Geometric Correspondence-Based Multimodal Learning for Ophthalmic Image Analysis.

Color fundus photography (CFP) and Optical coherence tomography (OCT) images are two of the most widely used modalities in the clinical diagnosis and management of retinal diseases. Despite the widespread use of multimodal imaging in clinical practice, few methods for automated diagnosis of eye diseases utilize correlated and complementary information from multiple modalities effectively. This paper explores how to leverage the information from CFP and OCT images to improve the automated diagnosis of retinal diseases. We propose a novel multimodal learning method, named geometric correspondence-based multimodal learning network (GeCoM-Net), to achieve the fusion of CFP and OCT images. Specifically, inspired by clinical observations, we consider the geometric correspondence between the OCT slice and the CFP region to learn the correlated features of the two modalities for robust fusion. Furthermore, we design a new feature selection strategy to extract discriminative OCT representations by automatically selecting the important feature maps from OCT slices. Unlike the existing multimodal learning methods, GeCoM-Net is the first method that formulates the geometric relationships between the OCT slice and the corresponding region of the CFP image explicitly for CFP and OCT fusion. Experiments have been conducted on a large-scale private dataset and a publicly available dataset to evaluate the effectiveness of GeCoM-Net for diagnosing diabetic macular edema (DME), impaired visual acuity (VA) and glaucoma. The empirical results show that our method outperforms the current state-of-the-art multimodal learning methods by improving the AUROC score 0.4%, 1.9% and 2.9% for DME, VA and glaucoma detection, respectively.

Metabolomics investigation on the volatile and non-volatile composition in enzymatic hydrolysates of Pacific oyster (Crassostrea gigas).

To investigate the differences of volatile and non-volatile metabolites between oyster enzymatic hydrolysates and boiling concentrates, molecular sensory analysis and untargeted metabolomics were employed. "Grassy," "fruity," "oily/fatty," "fishy," and "metallic" were identified as sensory attributes used to evaluate different processed oyster homogenates. Sixty-nine and 42 volatiles were identified by gas chromatography-ion mobility spectrometry and gas chromatography-mass spectrometry, respectively. Pentanal, 1-penten-3-ol, hexanal, (E)-2-pentenal, heptanal, (E)-2-hexenal, 4-octanone, (E)-4-heptenal, 3-octanone, octanal, nonanal, 1-octen-3-ol, benzaldehyde, (E)-2-nonenal, and (E, Z)-2,6-nonadienal were detected as the key odorants (OAV > 1) after enzymatic hydrolysis. Hexanal, (E)-4-heptenal, and (E)-2-pentenal were significantly associated with off-odor, and 177 differential metabolites were classified. Aspartate, glutamine, alanine, and arginine were the key precursors affecting the flavor profile. Linking sensory descriptors to volatile and nonvolatile components of different processed oyster homogenates will provide information for the process and quality improvement of oyster products.

Identification and validation of core microbes associated with key aroma formation in fermented pepper paste (Capsicum annuumL.).

Fermented peppers are usually obtained by the spontaneous fermentation of microorganisms attached to fresh peppers, and the variable microbial composition would lead to inconsistencies in flavor between batches. To demonstrate the roles of microorganisms in flavor formation, the core microbes closely associated with the key aroma compounds of fermented pepper paste were screened and validated in this study. Lactobacillus was the dominant bacterial genus in fermented pepper paste, whereas the main fungal genera were Alternaria and Kazachstania. Nine strains of the genera Lactobacillus, Weissella, Bacillus, Zygosaccharomyces, Kazachstania, Debaryomyces, and Pichia were isolated from fermented pepper paste. Eleven key aroma compounds were identified using gas chromatography combined with olfactometry and relative odor activity values. Correlation analysis showed that Zygosaccharomyces and Kazachstania were positively correlated with the majority of the key aroma compounds, whereas Lactobacillus was negatively correlated with them. Thus, Zygosaccharomyces and Kazachstania were identified as core genera associated with the key odorants. Finally, Zygosaccharomyces bisporus, Kazachstania humilis, and Lactiplantibacillus plantarum were used as starter cultures for fermented peppers, confirming that Z. bisporus and K. humilis were more beneficial for the key aroma compounds (e.g., acetate, linalool, and phenyl ethanol) rather than L. plantarum. This study contributed to understanding the flavor formation mechanism and provided references for the quality control of food fermentation.

Capturing the impact of oral processing behavior and bolus formation on the dynamic sensory perception and composition of steamed sturgeon meat.

The effect of oral processing on flavor release and change in composition of steamed sturgeon meat was investigated. Oral processing caused changes in the concentrations of taste compounds including amino acids, 5'-nucleotides, organic acids, and Na+. Sensory omics demonstrated that the concentrations of 12 volatile compounds increased significantly (p < 0.05) during the initial stage of oral processing. There is no significant difference in microstructure, texture, and particle size of meat bolus. The top fifteen differential lipids which including eight phospholipids in all processed samples significantly (p < 0.05) correlated with the flavor release. A total of 589 differential proteins were detected in three samples with different chewing times (0, 12, and 30 s). Analysis of the correlations between odorants and 19 differential proteins was performed. Enriched pathways including fatty acid degradation, valine, leucine and isoleucine degradation, glycine, serine and threonine metabolism, and arachidonic acid metabolism were associated with flavor release during oral processing. This study aimed to investigate potential links between flavor release and biological processes during oral processing from a proteomics perspective.