Physico-chemical quality parameters and overall quality index of apple during storage

The aim of this work was to determine the composi on, physicochemical and quality parameters of Chemlal EVOO from di erent regions of Algeria and at di erent harvest me. Olive-oil yield, quality indices, fa y acid composition, pigments (carotenoids and chlorophylls), phenolic compounds were evaluated for a complete descrip on of olive-oil samples. The nal results showed that the altitude and the me of harvest obviously had an in uence on the quality and the chemical composition of the olive oils. A very advanced maturity was observed for the olives coming from the low altitudes. The olives of the region of Oran at 80 m of al tude ripen very quickly that the olives of M'chedallah to 474 m and more than those of the region of Illit- en which is more than 700 m. The pro le of fa y acids was in uenced by al tude. Oleic acid, which is a nutri onal and quality criterion for olive oil, increases with al tude but does not exceed the limit set by the Interna onal Olive Council. On the other hand, the content of pigments and phenolic compounds, decreased with the matura on of olives. According to the results found, the most suitable ripening index for harves ng olives for Chemlal EVOOs of high chemical quality starts from 3.20 up to 4.

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Analyzing Physico - Chemical Quality Parameters of Abay River Near To Bahir Dar, Ethiopia

10.7176/cer/12-2-02 ◽

2020 ◽

Keyword(s):

Quality Parameters ◽

Chemical Quality ◽

Bahir Dar ◽

Physico Chemical

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Physico-Chemical Quality Assessment of Various Bottled Commercial Water Brands in Okada Town, Edo State, Nigeria

Current Journal of Applied Science and Technology ◽

10.9734/cjast/2021/v40i1531407 ◽

2021 ◽

pp. 1-8

Author(s):

F. W. Ngubi ◽

I. Eiroboyi

Keyword(s):

Drinking Water ◽

Water Samples ◽

Quality Parameters ◽

Bottled Water ◽

World Health ◽

Total Hardness ◽

Human Consumption ◽

Chemical Quality ◽

Physico Chemical ◽

Health Organization

In this study, Physico-chemical assessment of some commercial drinking water sold in bottles in Okada Town was evaluated to ascertain their compliance with World Health Organization (WHO) and Nigerian Industrial Standard (NIS): Nigerian Standard for Drinking Water Quality threshold limits using standard analytical methods. Seven different bottled water samples obtained from different manufacturers labelled BWA to BWG were analyzed physically and chemically. Physical examination of the samples showed that they were odourless, colourless, and tasteless. Chemical quality parameters examined were pH, Chloride (Cl-), total hardness (TS), Phosphate (PHO3-), Nitrate (NO3-), Sulphate (SO42-), Iron (Fe), Potassium (K), Sodium (Na), Manganese (Mn), Zinc (Zn), total dissolved solids (TDS), conductivity, turbidity, and total suspended solids (TSS). The pH values of 57.1% of the water samples (BWA, BWB, BWC, BWE & BWF) were within the standards. The remaining chemical quality parameters (Cl-, TS, PHO3-, NO3-, Sulphate SO42-, Iron Fe, K, NA, Mn, Zn, TDS, Conductivity, turbidity, and TSS) of the branded bottled water samples were within the standards for clean and safe drinking. Therefore, they were considered safe and fit for human consumption.

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Effect of Tithonia diversifolia on Compost Pile Heat Built-Up and Physico-Chemical Quality Parameters of Chimato Compost

Environment and Natural Resources Research ◽

10.5539/enrr.v3n3p63 ◽

2013 ◽

Vol 3 (3) ◽

Cited By ~ 1

Author(s):

Angstone Noel J. Thembachako Mlangeni

Keyword(s):

Quality Parameters ◽

Chemical Quality ◽

Tithonia Diversifolia ◽

Physico Chemical

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Online Prediction of Physico-Chemical Quality Attributes of Beef Using Visible—Near-Infrared Spectroscopy and Chemometrics

Foods ◽

10.3390/foods8110525 ◽

2019 ◽

Vol 8 (11) ◽

pp. 525 ◽

Cited By ~ 2

Author(s):

Amna Sahar ◽

Paul Allen ◽

Torres Sweeney ◽

Jamie Cafferky ◽

Gerard Downey ◽

...

Keyword(s):

Near Infrared ◽

Cross Validation ◽

Nir Spectroscopy ◽

Quality Parameters ◽

Coefficient Of Determination ◽

Drip Loss ◽

Chemical Quality ◽

Post Mortem ◽

Physico Chemical ◽

Online Prediction

The potential of visible–near-infrared (Vis–NIR) spectroscopy to predict physico-chemical quality traits in 368 samples of bovine musculus longissimus thoracis et lumborum (LTL) was evaluated. A fibre-optic probe was applied on the exposed surface of the bovine carcass for the collection of spectra, including the neck and rump (1 h and 2 h post-mortem and after quartering, i.e., 24 h and 25 h post-mortem) and the boned-out LTL muscle (48 h and 49 h post-mortem). In parallel, reference analysis for physico-chemical parameters of beef quality including ultimate pH, colour (L, a*, b*), cook loss and drip loss was conducted using standard laboratory methods. Partial least-squares (PLS) regression models were used to correlate the spectral information with reference quality parameters of beef muscle. Different mathematical pre-treatments and their combinations were applied to improve the model accuracy, which was evaluated on the basis of the coefficient of determination of calibration (R2C) and cross-validation (R2CV) and root-mean-square error of calibration (RMSEC) and cross-validation (RMSECV). Reliable cross-validation models were achieved for ultimate pH (R2CV: 0.91 (quartering, 24 h) and R2CV: 0.96 (LTL muscle, 48 h)) and drip loss (R2CV: 0.82 (quartering, 24 h) and R2CV: 0.99 (LTL muscle, 48 h)) with lower RMSECV values. The results show the potential of Vis–NIR spectroscopy for online prediction of certain quality parameters of beef over different time periods.

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Application of Artificial Neural Network to Predict the Water Quality Index of Brahmani River Basin Odisha, India

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v6i8.141 ◽

2018 ◽

Vol 6 (8) ◽

pp. 205

Author(s):

Binayini Bhagat ◽

D. P. Satapathy

Keyword(s):

Neural Network ◽

Water Quality ◽

Artificial Neural Network ◽

Surface Water ◽

Water Quality Index ◽

Quality Index ◽

Oxygen Demand ◽

Quality Parameters ◽

Physico Chemical ◽

Artificial Neural

Water is one of the prime elements responsible for subsistence on the earth. The scarcity of potable water is gradually increasing with the increase in population. The surface water quality is a very crucial and sensitive issue and is also a great environmental concern worldwide. Surface water pollution by physical, chemical, radiological and biological contaminants can be considered as an epidemic at times, all over the world. The present research work aims at assessing the water quality index (WQI) in the surface water of Brahmani river basin in Odisha by monitoring five sampling locations. The surface water samples data were subjected to comprehensive physico-chemical analysis besides general parameters. The monthly water quality parameters were collected and analyzed from five selected gauging stations of Odisha during the months of January to December from 2011 to 2016. Eleven physical, chemical and biological water quality parameters viz. pH, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Electrical Conductivity(EC), Nitrogen as nitrate (Nitrate-N), Total Coli-form Bacteria(TC), Fecal Coli-form Bacteria(FC), Chemical Oxygen Demand (COD), Nitrogen as ammonia (NH4-N), Total Alkalinity (TA) as CaCO3, Total Hardness (TH) as CaCO3 were selected for the analysis. Analysis of water quality for Brahmani River is done by Water Quality Index (WQI). Prediction of water quality index is done by using Artificial Neural Network (ANN). It is apparent from WQI values that Talcher and Panposh recorded the water quality as moderate to poor and nearly unsuitable during the years 2011-2016 indicating water as not safe for domestic purposes and needs treatment, the WQI values of Kamalanga ranged from good to poor and the WQI values of Aul and Pottamundai ranged from good to moderate. Eleven physico-chemical parameters were involved in this analysis as input variables and water quality index as output variable. Two models were proposed to identify the most effective model in an attempt to predict the WQI. Correlation between the parameters was carried out to find out the significant parameters affecting WQI. The ANN developed was trained and tested successfully using the available data sets and the performance of ANN models were determined by coefficient of determination (R2) and Root Mean Square Error (RMSE). Results show that ANN-1 gives the higher value of R2 in summer, monsoon and winter season (0.989, 0.976 and 0.959) and low RMSE (2.1865, 2.0768 and1.9657) as compared to that of the second model (ANN-2) which gives R2 value as 0.933, 0.945 and 0.943 and RMSE value as 2.8765, 2.5456 and 1.2745 for summer, monsoon and winter seasons respectively. Hence this study triggered the use of Artificial Neural Network to predict the Water Quality Index (WQI) rather than using the traditional WQI equation.

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