Journal: Current Research in Food Science

Article Title: Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

doi: 10.1016/j.crfs.2021.01.002

Figure Lengend Snippet: Random Forest (RF) applications in hyperspectral image analysis of food products.

Article Snippet: Determination of honey floral origin , 400–1000 , – , Image thresholding , – , 70:30 , MATLAB R2012a , 92% , .

Techniques: Software, Infection

Journal: Current Research in Food Science

Article Title: Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

doi: 10.1016/j.crfs.2021.01.002

Figure Lengend Snippet: k-Nearest Neighbor (k-NN) applications in hyperspectral image analysis of food products.

Article Snippet: Determination of honey floral origin , 400–1000 , – , Image thresholding , – , 70:30 , MATLAB R2012a , 92% , .

Techniques: Software

Journal: MicrobiologyOpen

Article Title: Genomic instability of TnSMU2 contributes to Streptococcus mutans biofilm development and competence in a cidB mutant

doi: 10.1002/mbo3.934

Figure Lengend Snippet: CFU and COMSTAT analysis of wild‐type, cidB rough, and cidB smooth biofilms. Quantification of viable biofilm cells by CFU serial dilution plating (a) was performed on 48 hr UA159 (wild‐type), cidB rough, and cidB smooth anaerobic biofilms. Data represent n = 3 biological replicates, error bars = SEM . * represents significant difference compared to wild type ( p < .01, Holm–Sidak). Total biomass (b), average biofilm thickness (c), and the roughness coefficient (d) were quantified using pixel density and calculated through the COMSTAT algorithm (Heydorn et al., ) in MATLAB. The roughness coefficient represents the heterogeneity of the biofilm surface, with higher values indicative of a less uniform surface. Light bars represent biofilms generated during CO 2 growth, and dark bars represent biofilms generated during anaerobic conditions. Data represent the average of n = 18–27 random fields of view acquired over n = 3–5 independent experiments. Error bars = standard error of the mean ( SEM ), * represents significant difference between CO 2 growth conditions compared to wild type ( p < .05, Holm–Sidak for biomass and thickness, Dunn's test for Roughness coefficient), ** represents significant difference between anaerobic growth conditions compared to wild type ( p < .05, Dunn's test), # represents significant difference between anaerobic wild type and CO 2 wild type ( p < .001, Mann–Whitney rank sum test)

Article Snippet: Quantification of biofilm statistics was performed using COMSTAT (Heydorn et al., ) running on MATLAB R2010a (MathWorks) with manual thresholding on individual images collected on separate days.

Techniques: Serial Dilution, Generated, MANN-WHITNEY