scholarly journals Use of multivariate statistics to predict the physicochemical quality of milk

2020 ◽  
Vol 9 (4) ◽  
pp. e41942808
Author(s):  
Lenara Oliveira Pinheiro ◽  
Mário Roberto Júnior ◽  
Clara Mariana Gonçalves Lima ◽  
Heliara Caires Sousa ◽  
Jorge Pamplona Pagnossa ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Raw Milk ◽  
Dry Extract ◽  
Quality Of Food ◽  
Quality Of Milk ◽  
Main Components ◽  
Extract Protein

Multivariate analysis involves the application of statistical and computational methods to predict responses. Among the various methods of statistical analysis multivariate, the analysis by main components is highlighted to predict the composition and quality of food in general. The objective of this work was to characterize the milk producers of the municipality of Itapetinga-BA, using principal component analysis. Twenty samples of raw milk were used, collected at the reception of the dairy located in Itapetinga-BA. The variables analyzed were: fat, density, defatted dry extract, protein and lactose. The first two main components explained 87.24% of the total variation. It was verified the formation of different groups distributed in the four quadrants of the system. First quadrant stood out from the others by forming a group composed of ten producers in the analyzed region, characterized by presenting samples with higher lactose content and lower fat content in milk. The lactose and fat variables are of greater importance in the characterization of milk.

Evaluation of Sweet Sauce Characteristic Taste Based on Electronic Tongue Technology

2012 ◽  
Vol 554-556 ◽  
pp. 1593-1601
Author(s):  
Ming Quan Huang ◽  
Lu Wang ◽  
Bao Guo Sun ◽  
Hong Yu Tian
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Reducing Sugars ◽  
Electronic Tongue ◽  
Amino Nitrogen ◽  
Physicochemical Characterization ◽  
Principal Component ◽  
On Line

A commercial electronic tongue (ET) with specific sensors was applied on taste distinction and physicochemical characterization of seven kinds of sweet sauces. The response signals of ET sensors were analyzed by Principal Component Analysis (PCA) and Discriminant Factor Analysis (DFA). Meanwhile, these signals were transformed into the four relative taste scores (sourness, saltiness, umami and sweetness) by macro operation, followed by comparing with the corresponding four physiochemical indexes (total acids, sodium chloride, amino nitrogen and reducing sugars) which were determined by the methods in GB/T. The results show that ET can be used to distinguish different kinds of sweet sauces according to overall taste. Moreover, the intensity order of taste scores that obtained from ET is basically matched with the sequence of the corresponding physicochemical indexes, which proves that ET technique can be an effective approach to monitor and guarantee the quality of sweet sauce on line.

Research on Hydrochemistrical Characteristics of Shallow Groundwater from Huainan City

2013 ◽  
Vol 726-731 ◽  
pp. 1367-1372 ◽  
Author(s):  
Xiao Wen He ◽  
Guang Quan Xu ◽  
Wei Ning Wang
Keyword(s):  
Heavy Metals ◽  
Principal Component Analysis ◽  
Shallow Groundwater ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Characteristics ◽  
Groundwater Samples ◽  
Different Types ◽  
Main Components

Based on previous studies, 130 shallow groundwater samples are collected from Huainan city according to some rules, and 21 indexes of the groundwater samples are tested by different instruments. Moreover, the important works have been done, including statistical characteristics of groundwater components, and divided different types of hydrochemistry, analyses of the relationships between the hardness and Ca2+/Na+, Mg2+/Na+, TDS and the ES. The correlation between heavy metals and conventional components has been discussed. Finally, main components have influence on the quality of shallow groundwater by the method of principal component analysis.

scholarly journals A Methodology Based on FT-IR Data Combined with Random Forest Model to Generate Spectralprints for the Characterization of High-Quality Vinegars

Foods ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1411
Author(s):  
José Luis P. Calle ◽  
Marta Ferreiro-González ◽  
Ana Ruiz-Rodríguez ◽  
Gerardo F. Barbero ◽  
José Á. Álvarez ◽  
...  
Keyword(s):  
Random Forest ◽  
Raw Materials ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Raw Material ◽  
Support Vector ◽  
Protected Designation Of Origin ◽  
Ft Ir

Sherry wine vinegar is a Spanish gourmet product under Protected Designation of Origin (PDO). Before a vinegar can be labeled as Sherry vinegar, the product must meet certain requirements as established by its PDO, which, in this case, means that it has been produced following the traditional solera and criadera ageing system. The quality of the vinegar is determined by many factors such as the raw material, the acetification process or the aging system. For this reason, mainly producers, but also consumers, would benefit from the employment of effective analytical tools that allow precisely determining the origin and quality of vinegar. In the present study, a total of 48 Sherry vinegar samples manufactured from three different starting wines (Palomino Fino, Moscatel, and Pedro Ximénez wine) were analyzed by Fourier-transform infrared (FT-IR) spectroscopy. The spectroscopic data were combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) and principal component analysis (PCA), as well as other nonparametric supervised techniques, namely, support vector machine (SVM) and random forest (RF), for the characterization of the samples. The HCA and PCA results present a clear grouping trend of the vinegar samples according to their raw materials. SVM in combination with leave-one-out cross-validation (LOOCV) successfully classified 100% of the samples, according to the type of wine used for their production. The RF method allowed selecting the most important variables to develop the characteristic fingerprint (“spectralprint”) of the vinegar samples according to their starting wine. Furthermore, the RF model reached 100% accuracy for both LOOCV and out-of-bag (OOB) sets.

Proteinase and phospholipase activities and development at different temperatures of yeasts isolated from bovine milk

2011 ◽  
Vol 78 (4) ◽  
pp. 385-390 ◽  
Author(s):  
Priscilla A Melville ◽  
Nilson R Benites ◽  
Monica Ruz-Peres ◽  
Eugenio Yokoya
Keyword(s):  
Dairy Products ◽  
Room Temperature ◽  
Bovine Milk ◽  
Raw Milk ◽  
Pathogenic Potential ◽  
Proteinase Production ◽  
Different Temperatures ◽  
Quality Of Milk ◽  
Means Of Transmission

The presence of yeasts in milk may cause physical and chemical changes limiting the durability and compromising the quality of the product. Moreover, milk and dairy products contaminated by yeasts may be a potential means of transmission of these microorganisms to man and animals causing several kinds of infections. This study aimed to determine whether different species of yeasts isolated from bovine raw milk had the ability to develop at 37°C and/or under refrigeration temperature. Proteinase and phospholipase activities resulting from these yeasts were also monitored at different temperatures. Five genera of yeasts (Aureobasidium sp., Candida spp., Geotrichum spp., Trichosporon spp. and Rhodotorula spp.) isolated from bovine raw milk samples were evaluated. All strains showed one or a combination of characteristics: growth at 37°C (99·09% of the strains), psychrotrophic behaviour (50·9%), proteinase production (16·81% of the strains at 37°C and 4·09% under refrigeration) and phospholipase production (36·36% of the isolates at 37°C and 10·9% under refrigeration), and all these factors may compromise the quality of the product. Proteinase production was similar for strains incubated at 37°C (16·81% of the isolates) and room temperature (17·27%) but there was less amount of phospholipase-producing strains at room temperature (15·45% of the isolates were positive) when compared with incubation at 37°C (36·36%). Enzymes production at 37°C by yeasts isolated from milk confirmed their pathogenic potential. The refrigeration temperature was found to be most efficient to inhibit enzymes production and consequently ensure better quality of milk. The viability of yeasts and the activity of their enzymes at different temperatures are worrying because this can compromise the quality of dairy products at all stages of production and/or storage, and represent a risk to the consumer.

scholarly journals Sensory Profile of Chihuahua Cheese Manufactured from Raw Milk

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Sarai Villalobos-Chaparro ◽  
Erika Salas-Muñóz ◽  
Néstor Gutiérrez-Méndez ◽  
Guadalupe Virginia Nevárez-Moorillón
Keyword(s):  
Principal Component Analysis ◽  
Geographical Location ◽  
Principal Component ◽  
Raw Milk ◽  
Component Analysis ◽  
Sensory Profile ◽  
Different Seasons ◽  
Quantitative Descriptive ◽  
Traditional Manufacturing ◽  
Chihuahua Cheese

Chihuahua cheese is a local artisanal cheese traditionally produced from raw milk. When this cheese is produced with pasteurized milk, cheesemakers complain that there are differences in taste and aroma as compared with traditional manufacturing. This work aimed to obtain a descriptive sensory analysis of Chihuahua cheese manufactured with raw milk under traditional conditions. Samples were collected in five cheese dairies at two different seasons (summer and autumn), and a Quantitative Descriptive Sensorial Analysis was done by a panel of trained judges. For aroma descriptors, cooked descriptor showed differences between dairies, and whey was different among dairies and sampling seasons (P<0.01); diacetyl, fruity (P<0.01), as well as free fatty acids, nutty and sulphur (P<0.05) descriptors varied between seasons. For flavour descriptors, bitter perception was different between dairies and seasons (P<0.01). Salty and creamy cheese was also different among dairies (P<0.01). A Principal Component Analysis for differences among dairies and sampling season demonstrated that the first three components accounted for 90% of the variance; variables were more affected by the sampling seasons than by the geographical location or if the dairy was operated by Mennonites. Chihuahua cheese sensorial profile can be described as a semi-matured cheese with a bitter flavour, slightly salted, and with a cream flavour, with aroma notes associated with whey and sour milk. Principal Component Analysis demonstrated season influence on flavour and aroma characteristics.

scholarly journals Validation of the Arabic version of the “12-item short-form health survey” (SF-12) in a sample of Lebanese adults

2021 ◽  
Vol 79 (1) ◽  
Author(s):  
Chadia Haddad ◽  
Hala Sacre ◽  
Sahar Obeid ◽  
Pascale Salameh ◽  
Souheil Hallit
Keyword(s):  
Quality Of Life ◽  
Principal Component Analysis ◽  
Psychometric Properties ◽  
Short Form ◽  
Principal Component ◽  
Arabic Version ◽  
Short Form Health Survey ◽  
Form Health ◽  
Short Form Health

Abstract Background In clinical practice, quality of life measures can be used alongside some types of assessment to give valuable information that can identify areas that influence an individual and help the clinician make the best healthcare choices. This study aimed to investigate the psychometric properties of the Arabic version of the 12-item short-form health survey (SF-12) in a sample of Lebanese adults. Methods This cross-sectional study performed between July and November 2019 recruited 269 participants. Cronbach’s alpha was used to assess the reliability of the SF-12 questionnaire, and a factor analysis using the principal component analysis was performed to confirm its construct validity. Results The mean score for the “physical component summary (PCS-12)” was 50.27 ± 8.94 (95 % CI: 49.18–51.36) and for the “Mental component summary (MCS-12)” was 44.95 ± 12.17 (95 % CI: 43.47–46.43). A satisfactory Cronbach’s alpha was found for the two components: MCS (α = 0.707) and PCS (α = 0.743). The principal component analysis converged over a two-factor solution (physical and mental), explaining a total variance of 55.75 %. Correlations between the SF-12 scales and single items were significant, showing a good construct validity. The “physical functioning”, “role physical”, “bodily pain”, and “general health” subscales were highly associated with “PCS-12”, while the “vitality”, “social functioning”, “role emotional”, and “mental health” subscales were more associated with MCS-12. Conclusions The Arabic version of the SF-12 is a reliable, easy-to-use, and valid tool to measure health-related quality of life in the general population. Future studies using a larger sample size and focusing on questionnaire psychometric properties are necessary to confirm our findings.

Availability and Spatial Inequality of Rural Infrastructure in Jungle Mahal Blocks of Purulia District, India

2021 ◽  
pp. 232102492110082
Author(s):  
Uttam Kumar Patra ◽  
Suman Paul
Keyword(s):  
Quality Of Life ◽  
Principal Component Analysis ◽  
Regional Development ◽  
Principal Component ◽  
Spatial Inequality ◽  
Rural Infrastructure ◽  
Financial Infrastructure ◽  
Purulia District ◽  
Dimension Index

Rural infrastructure is fundamental and central to the concept of quality of life as well as human development. The major characteristic of regional development is the constant widening of regional disparity in India after different plan period. Various Finance Commissions and Planning Commissions laid emphasis on the objective of achieving balanced regional development. The article identifies a gap in terms of education, health, communication and financial infrastructure in the study of panchayats of Jungle Mahal blocks. Mapping of regional disparities can aid in effective policymaking at the preliminary stage of planning. Panchayat level inequality has been analysing using dimension index and principal component analysis (PCA). Wide disparities in the availability of rural infrastructure have been pointed out and proper recommendation has also been made to minimise the gap in spatial inequality.

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