Food safety is a global health concern. For the prevention and recognition of problems related to health and safety, detection of foodborne pathogen is of utmost importance at all levels of food production chain. For several decades, a lot of research has been targeted at the development of rapid methodology as reducing the time needed to complete pathogen detection tests has been the primary goal of food microbiologists. With the result, food microbiology laboratories now have a wide array of detection methods and automated technologies such as enzyme immunoassay, polymerase chain reaction, and microarrays, which can cut test times considerably. Nucleic acid amplification strategies and advances in amplicon detection methodologies have been the key factors in the progress of molecular microbiology. A comprehensive literature survey has been carried out to give an overview in the field of foodborne pathogen detection. In this paper, we describe the conventional methods, as well as recent developments in food pathogen detection, identification, and quantification, with a major emphasis on molecular detection methods.

High temperatures present a formidable challenge to the cultivation of hot pepper, profoundly impacting not only vegetative growth but also leading to flower and fruit abscission, thereby causing a significant reduction in yield. To unravel the intricate genetic mechanisms governing heat tolerance in hot pepper, an F₂ population was developed through the crossing of two distinct genotypes exhibiting contrasting heat tolerance characteristics: DLS-161-1 (heat tolerant) and DChBL-240 (heat susceptible). The F₂ population, along with the parental lines, was subjected to comprehensive phenotyping encompassing diverse morphological, physiological, and biochemical heat-related traits under high temperature conditions (with maximum temperature ranging from 31 to 46.5°C and minimum temperature from 15.4 to 30.5°C). Leveraging the Illumina Nova Seq-6000 platform, Double digest restriction-site associated DNA sequencing (ddRAD-seq) was employed to generate 67.215 Gb data, with subsequent alignment of 218.93 million processed reads against the reference genome of Capsicum annuum. Subsequent variant calling and ordering resulted in 5806 polymorphic SNP markers grouped into 12 LGs. Further QTL analysis identified 64 QTLs with LOD values ranging from 2.517 to 11.170 and explained phenotypic variance ranging from 4.05 to 19.39%. Among them, 21 QTLs, explaining more than 10% phenotypic variance, were identified as major QTLs controlling 9 morphological, 3 physiological, and 2 biochemical traits. Interestingly, several QTLs governing distinct parameters were found to be colocalized, suggesting either a profound correlation between the QTLs regulating these traits or their significant genomic proximity. In addition to the QTLs, we also identified 368380 SSR loci within the identified QTL regions, dinucleotides being the most abundant type (211,381). These findings provide valuable insights into the genetics of heat tolerance in hot peppers. The identified QTLs and SSR markers offer opportunities to develop heat-tolerant varieties, ensuring better crop performance under high-temperature conditions.

Bitter gourd (Momordica charantia L.) is an important vine crop of the Cucurbitaceae family and is well known for its high nutritional and medicinal values. However, the genetic variation remains largely unknown. Herein, 96 diverse bitter gourd genotypes were undertaken for diversity analysis using 10 quantitative traits, and 82 simple sequence repeat (SSR) markers. Out of 82 SSRs, 33 were polymorphic and the mean polymorphism information content (PIC) value was 0.38. Marker, JY-003 revealed a maximum (0.81) PIC value and, the number of alleles per locus ranged from 2 to 7 (average 3.46). The value of gene diversity showed the presence of a significant level of polymorphism among these genotypes. The unweighted pair group method (UPGMA) cluster analysis grouped the genotypes into two major clusters of which Cluster I comprised mostly small and medium-fruited genotypes of both M. charantia var. charantia and M. charantia var. muricata, whereas Cluster II included mostly long and extra-long fruited genotypes. Furthermore, these genotypes were divided into six distinct groups based on population structure analysis. The diversity analysis based on 10 quantitative traits revealed that earliness and high-yielding ability were exhibited by the predominantly gynoecious line DBGS-21-06 followed by DBGS-48-00. The principal component analysis (PCA) revealed that the first two components exhibited more than 50% of the total genetic variation. The present study deciphered a higher magnitude of agro-morphological and genetic diversity in 96 bitter gourd genotypes. Therefore, trait-specific genotypes identified in this study could be utilized in breeding programmes directed towards the development of improved cultivars and hybrids of bitter gourd.

The present study was undertaken to know the genetics of purple color of cauliflower curds using a Sicilian purple ‘PC-1’ and a white curding mid-late group genotype of Indian cauliflower. For this, a cross was attempted between ‘DC-466’ (white curd) and ‘PC-1’ (purple curd) and observed intermediate level of purple pigmentation on curds in F₁ plants. Segregation of F₂ population (173) revealed that the purple color of the curd was governed by a single gene dominant over white, but the expression of trait was incomplete. It was substantiated by segregation of plants of BC₁ and F_{2:3(intermediate)} generations into 1(white):1(intermediate) and 1(white):2(intermediate):1(intense), respectively. The F₂, B₁, and B₂ generations segregated into purple(intermediate to intense): white curding plants in the ratio of 126: 47, 26:24, and 40:0, respectively fitting well with the Mendelian ratio of single gene for purple curds. However, purple pigmentation on curds ranged from very light to intense, which corroborated with the wide range of anthocyanin content in F₂ (3.81–48.21 mg/100 g fw). Out of three molecular markers from high resolution map of Pr gene in purple color cauliflower ‘Graffiti’, only BoMYB3 marker could distinguish purple and white curding parents but did not show co-segregation while investigated in F₂ population. Expression of BoMYB1 gene was up regulated in both the purple curd genotypes ‘PC-1’ and ‘Graffiti’ in comparison to white curded ‘DC-466’, while BoMYB2 gene was slightly upregulated in ‘PC-1’ but down regulated in ‘Graffiti’. Occurrence of ‘broccoli type’ F₂ individuals and their genetic stability in F_2:3 support the intermediate position of ‘Sicilian purple’ between broccoli (Calabrese) and cauliflower. There was not any correlation between curd coloration and pigmentation on apical leaf and stem portion, indicating difference of expression in ‘PC-1’ than ‘Graffiti’. The information obtained is useful for breeding anthocyanin rich attractive purple curding ‘specialty cauliflower’ for better consumer health and growers’ earnings.

Aflatoxin contamination of groundnut (Arachis hypogaea L.) is a serious problem through out the world which leads to significant economic losses every year. Invasion of groundnut seed by the aflatoxin-producing fungi Aspergillus flavus and Aspergillus parasiticus, and subsequent contamination with aflatoxins, may occur at any stage during the postharvest handling of its seeds, which necessitates development of suitable procedures for identification of the pathogen. An identification key for different fungal isolates was developed on the basis of macromorphological features viz. diameter, elevation, margins, texture of the colony as well as colour of the colony from the top and reverse of the plate, extent of sporulation (low, medium and heavy) and presence and absence of radial sulcation after seven days of culture. Microscopic features such as shape and size of conidia, vesicles and hyphae have also been included in the key. On the basis of this key different Aspergillus isolates viz.A. Niger, A. fumigatus, A flavus, A. terreus, A. parasiticus, A. oryzae could be differentiated thereby showing a potential for detection of aflatoxigenic fungi in the infected lot.

Potato is a globally significant crop, crucial for food security and nutrition. Assessing vital nutritional traits is pivotal for enhancing nutritional value. However, traditional wet lab methods for the screening of large germplasms are time- and resource-intensive. To address this challenge, we used near-infrared reflectance spectroscopy (NIRS) for rapid trait estimation in diverse potato germplasms. It employs molecular absorption principles that use near-infrared sections of the electromagnetic spectrum for the precise and rapid determination of biochemical parameters and is non-destructive, enabling trait monitoring without sample compromise. We focused on modified partial least squares (MPLS)-based NIRS prediction models to assess eight key nutritional traits. Various mathematical treatments were executed by permutation and combinations for model calibration. The external validation prediction accuracy was based on the coefficient of determination (RSQ_external), the ratio of performance to deviation (RPD), and the low standard error of performance (SEP). Higher RSQ_external values of 0.937, 0.892, and 0.759 were obtained for protein, dry matter, and total phenols, respectively. Higher RPD values were found for protein (3.982), followed by dry matter (3.041) and total phenolics (2.000), which indicates the excellent predictability of the models. A paired t-test confirmed that the differences between laboratory and predicted values are non-significant. This study presents the first multi-trait NIRS prediction model for Indian potato germplasm. The developed NIRS model effectively predicted the remaining genotypes in this study, demonstrating its broad applicability. This work highlights the rapid screening potential of NIRS for potato germplasm, a valuable tool for identifying trait variations and refining breeding strategies, to ensure sustainable potato production in the face of climate change.