Estimation de la fertilité potentielle des ferralsols par la couleur

2009 ◽  
Vol 89 (3) ◽  
pp. 331-342 ◽  
Author(s):  
B. Koné ◽  
S. Diatta ◽  
O. Sylvester ◽  
G. Yoro ◽  
C. Mameri ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Regression Models ◽  
Soil Management ◽  
Soil Samples ◽  
Chemical Components ◽  
Soil Color ◽  
Relative Contribution ◽  
Physico Chemical ◽  
Multiple Regression Models ◽  
Potassium Magnesium

A farmer-friendly method of determining the most suitable cultivation soils would help in transferring new integrated soil management technologies. The potential for using soil color (Munsell data) was tested by physico-chemical analysis of 1028 ferrallitic soil samples from 289 profiles unequally allocated above 7 deg N in Côte d’Ivoire. Soil hue variations in depth and along the toposequence revealed the existence of vertical and lateral gradients of soil hue. The relative contribution of the different descriptors (clay, sand, carbon, total nitrogen, total phosphorus, potassium, magnesium and calcium) to the three functions extracted using a discriminant analysis to differentiate the four groups of soils with different hues was evaluated as well as the analysis of variance to determine the possible groups number for each one of the descriptors. Differences between physico-chemical components of red (2.5YR and 5YR) and yellow (7.5YR and 10YR) soils were determined, especially for P, Mg and K in extension. A decreasing gradient of inherent soil fertility indicators with an increasing yellowness in soil hue was revealed using multiple regression models. The soils 2.5YR and 5YR were therefore deemed more appropriate for stable and sustainable agriculture.Key words: Hue, ferralsols, fertility, soil use

scholarly journals Sensorial, Melissopalynological and Physico-Chemical Characteristics of Honey from Babors Kabylia’s Region (Algeria)

Foods ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 225
Author(s):  
Asma Ghorab ◽  
María Shantal Rodríguez-Flores ◽  
Rifka Nakib ◽  
Olga Escuredo ◽  
Latifa Haderbache ◽  
...  
Keyword(s):  
Sensory Properties ◽  
Chemical Components ◽  
Chemical Characteristics ◽  
Pollen Spectrum ◽  
Botanical Origin ◽  
Flavonoid Content ◽  
Chemical Parameters ◽  
High Tendency ◽  
Physico Chemical ◽  
Pollen Types

This study aimed to characterize the honeys of Babors Kabylia through sensory, melissopalynological and physico-chemical parameters. Thirty samples of honey produced in this region were collected over a period of two years and analyzed. All the samples presented physico-chemical parameters in conformity with legislation on honey quality, with few exceptions, linked mainly to beekeeping management. The pollen spectrum revealed a great diversity with 96 pollen types. The main pollen types were spontaneous species as Fabaceae (Hedysarum, Trifolium, Genisteae plants), Asteraceae plants, Ericaceae (Erica arborea L.) or Myrtus and Pistacia. The sensory properties of samples showed a high tendency to crystallization, the colors were from white to brown, but most of them had gold color. Smell and odor corresponded mainly to vegetal and fruity families and in taste perceptions besides sweetness highlighted sourness and saltiness notes. Seventeen samples were polyfloral, one was from honeydew and twelve were monofloral from heather, genista plants, sulla, blackberry or Asteraceae. Heather and the honeydew samples showed the darkest color, the highest electrical conductivity and phenol and flavonoid content. A statistical analysis based on the most representative pollen types, sensory properties and some physico-chemical components allowed the differentiation of honey samples in terms of botanical origin.

scholarly journals Impact of Preharvest and Postharvest on Color Changes during Convective Drying of Mangoes

Foods ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 490
Author(s):  
Alioune Diop ◽  
Jean–Michel Méot ◽  
Mathieu Léchaudel ◽  
Frédéric Chiroleu ◽  
Nafissatou Diop Ndiaye ◽  
...  
Keyword(s):  
Regression Models ◽  
Convective Drying ◽  
Color Changes ◽  
Ripening Time ◽  
Physico Chemical ◽  
C 30 ◽  
The Impact

The purpose of this study was to evaluate the impact of the harvest stage, ripening conditions and maturity on color changes of cv. ‘Cogshall’ and cv. ‘Kent’ variety mangoes during drying. A total of four harvests were undertaken, and the fruits were ripened at 20 and 35 °C for five different ripening times at each temperature. At each ripening time, mangoes were dried at 60 °C/30% RH/1.5 m/s for 5 h. A wide physico-chemical and color variability of fresh and dry pulp was created. The relationships according to the L*, H* and C* coordinates were established using mixed covariance regression models in relation to the above pre- and postharvest (preprocess) parameters. According to the L* coordinate results, browning during drying was not affected by the preprocess parameters. However, dried slices from mangoes ripened at 35 °C exhibited better retention of the initial chroma, and had a greater decrease in hue than dried slices from mangoes ripened at 20 °C. However, fresh mango color, successfully managed by the pre- and postharvest conditions, had more impact on dried mango color than the studied parameters. The preprocess parameters were effective levers for improving fresh mango color, and consequently dried mango color.

scholarly journals 242 Effect of trucking distance on sale price of beef calf and feeder cattle lots sold through video auctions from 2010 through 2018

2020 ◽  
Vol 98 (Supplement_3) ◽  
pp. 10-11
Author(s):  
Esther D McCabe ◽  
Mike E King ◽  
Karol E Fike ◽  
Maggie J Smith ◽  
Glenn M Rogers ◽  
...  
Keyword(s):  
Multiple Regression ◽  
Regression Models ◽  
Average Weight ◽  
Sale Price ◽  
Feeder Cattle ◽  
A Value ◽  
Multiple Regression Models ◽  
Determine Effect

Abstract The objective was to determine effect of trucking distance on sale price of beef calf and feeder cattle lots sold through Superior Livestock Video Auctions from 2010 through 2018. Data analyzed were collected from 211 livestock video auctions. There were 42,043 beef calf lots and 19,680 feeder cattle lots used in these analyses. Six states (Colorado, Iowa, Kansas, Nebraska, Oklahoma, and Texas) of delivery comprised 70% of calf lots and 83% of feeder cattle lots and were used in these analyses. All lot characteristics that could be accurately quantified or categorized were used to develop multiple regression models that evaluated effects of independent factors using backwards selection. A value of P < 0.05 was used to maintain a factor in the final models. Based upon reported state of origin and state of delivery, lots were categorized into one of the following trucking distance categories: 1) Within-State, 2) Short-Haul, 3) Medium-Haul, and 4) Long-Haul. Average weight and number of calves in lots analyzed was 259.2 ± 38.4 kg BW and 100.6 ± 74.3 head, respectively. Average weight and number of feeder cattle in lots analyzed was 358.4 ± 34.3 kg BW and 110.6 ± 104.1 head, respectively. Beef calf lots hauled Within-State sold for more ($169.24/45.36 kg; P < 0.0001) than other trucking distance categories (Table 1). Long-Haul calf lots sold for the lowest (P < 0.0001) price ($166.70/45.36 kg). Within-State and Short-Haul feeder cattle lots sold for the greatest (P < 0.0001) price ($149.96 and $149.81/45.36 kg, respectively; Table 2). Long-Haul feeder cattle lots sold for the lowest (P < 0.0001) price, $148.43/45.36 kg. These results indicate there is a price advantage for lots expected to be hauled shorter distances, likely because of cost and risk associated with transportation.

scholarly journals Characterization of Ultra-Thin Diamond-Like Carbon Films by SEM/EDX

Coatings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 729
Author(s):  
Chanida Puttichaem ◽  
Guilherme P. Souza ◽  
Kurt C. Ruthe ◽  
Kittipong Chainok
Keyword(s):  
Regression Models ◽  
Hard Disk Drives ◽  
Carbon Films ◽  
Diamond Like Carbon ◽  
Disk Drives ◽  
Cross Sectional ◽  
Protective Films ◽  
High Throughput Method ◽  
Multiple Regression Models

A novel, high throughput method to characterize the chemistry of ultra-thin diamond-like carbon films is discussed. The method uses surface sensitive SEM/EDX to provide substrate-specific, semi-quantitative silicon nitride/DLC stack composition of protective films extensively used in the hard disk drives industry and at Angstrom-level. SEM/EDX output is correlated to TEM to provide direct, gauge-capable film thickness information using multiple regression models that make predictions based on film constituents. The best model uses the N/Si ratio in the films, instead of separate Si and N contributions. Topography of substrate/film after undergoing wear is correlatively and compositionally described based on chemical changes detected via the SEM/EDX method without the need for tedious cross-sectional workflows. Wear track regions of the substrate have a film depleted of carbon, as well as Si and N in the most severe cases, also revealing iron oxide formation. Analysis of film composition variations around industry-level thicknesses reveals a complex interplay between oxygen, silicon and nitrogen, which has been reflected mathematically in the regression models, as well as used to provide valuable insights into the as-deposited physics of the film.

scholarly journals A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

Multiple regression models for predicting total daily pollen concentration in Cartagena

Grana ◽  
2005 ◽  
Vol 44 (2) ◽  
pp. 108-114 ◽  
Author(s):  
José Manuel Angosto ◽  
Stella Moreno‐Grau ◽  
Javier Bayo ◽  
Belén Elvira‐Rendueles
Keyword(s):  
Multiple Regression ◽  
Regression Models ◽  
Pollen Concentration ◽  
Multiple Regression Models ◽  
Daily Pollen Concentration

The Effects of Prepaid Metering Systems on Customer Satisfaction in Niger State, Nigeria

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Ayooluwa Femi Aribisala ◽  
Musa Mohammed
Keyword(s):  
Customer Satisfaction ◽  
Regression Models ◽  
Response Rate ◽  
Spearman Correlation ◽  
Electricity Use ◽  
Customer Care ◽  
Level Of Satisfaction ◽  
Multiple Regression Models ◽  
The Relationship ◽  
Structured Questionnaire

Energy consumers in Nigeria have long complained about Distribution Companies' unfair billing practices, exorbitant monthly electricity bills resulting from meter estimation rather than accurate meter reading and calculation based on uninterrupted electricity use. The objectives of the study were to establish the relationship between the prepaid metering system and customer satisfaction; and to evaluate the level of satisfaction with respect to the usage of the prepaid metering system in Niger State, Nigeria. In carrying out the study, the structured questionnaire was administered to 393 randomly chosen respondents drawn from prepaid meter users, out of which 344 responded generating a response rate of 87.5%. The data derived were subjected to spearman correlation and multiple regression models. The major findings from the study showed a significant, moderate and positive relationship between the prepaid metering system and customer satisfaction. Additionally, three significant predictors, Affordability, Availability and Flexibility with p < .01 are statistically significant. Further findings from descriptive statistics revealed that users had the highest level of satisfaction with the privacy they had as a result of no meter readers and no accumulated. The study concluded that there exist a positive and beneficial link between the prepaid metering system and customer satisfaction. The study therefore recommends the provision of a smart metering system, good customer care units and a marketing campaign for better knowledge of the prepaid metering system.

Using absorbance peak of carbonate to select suitable regression model before predicting soil inorganic carbon concentration by mid-infrared reflectance spectroscopy

2021 ◽  
Author(s):  
Cécile Gomez ◽  
Tiphaine Chevallier ◽  
Patricia Moulin ◽  
Bernard G. Barthès
Keyword(s):  
Prediction Model ◽  
Regression Models ◽  
Inorganic Carbon ◽  
Reflectance Spectroscopy ◽  
Soil Samples ◽  
Infrared Reflectance ◽  
Mid Infrared ◽  
Soil Inorganic Carbon ◽  
Infrared Reflectance Spectroscopy ◽  
Absorbance Peak

&lt;p&gt;&lt;span&gt;Mid-Infrared reflectance spectroscopy (MIRS, 4000 &amp;#8211; 400 cm&lt;sup&gt;-1&lt;/sup&gt;) is being considered to provide accurate estimations of soil inorganic carbon (SIC) contents. Usually, the prediction performances by MIRS are analyzed using figures of merit based on entire test datasets characterized by large SIC ranges, without paying attention to performances at sub-range scales. This work aims to &lt;em&gt;1)&lt;/em&gt; evaluate the performances of MIR regression models for SIC prediction, for a large range of SIC test data (0-100 g/kg) and for several regular sub-ranges of SIC values (0-5, 5-10, 10-15 g/kg, etc.) and &lt;em&gt;2)&lt;/em&gt; adapt the prediction model depending on sub-ranges of test samples, using the absorbance peak at 2510 cm&lt;sup&gt;-1&lt;/sup&gt; for separating SIC-poor and SIC-rich test samples. This study used a Tunisian MIRS topsoil dataset including 96 soil samples, mostly rich in SIC, to calibrate and validate SIC prediction models; and a French MIRS topsoil dataset including 2178 soil samples, mostly poor in SIC, to test them. Two following regression models were used: a partial least squares regression (PLSR) using the entire spectra and a simple linear regression (SLR) using the height of the carbonate absorbance peak at 2150&amp;#160;cm&lt;sup&gt;-1&lt;/sup&gt;.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;First, our results showed that PLSR provided &lt;em&gt;1) &lt;/em&gt;better performances than SLR on the Validation Tunisian dataset (R&lt;sup&gt;2&lt;/sup&gt;&lt;sub&gt;test&lt;/sub&gt; of 0.99 vs. 0.86, respectively), but &lt;em&gt;2) &lt;/em&gt;lower performances than SLR on the Test French dataset (R&lt;sup&gt;2&lt;/sup&gt;&lt;sub&gt;test&lt;/sub&gt; of 0.70 vs. 0.91, respectively). Secondly, our results showed that on the Test French dataset, predicted SIC values were more accurate for SIC-poor samples (&lt; 15 g/kg) with SLR (RMSE&lt;sub&gt;test&lt;/sub&gt; from 1.5 to 7.1 g/kg, depending on the sub-range) than with PLSR prediction model (RMSE&lt;sub&gt;test &lt;/sub&gt;from 7.3 to 14.8 g/kg, depending on the sub-range). Conversely, predicted SIC values were more accurate for carbonated samples (&gt; 15 g/kg) with PLSR (RMSE&lt;sub&gt;test&lt;/sub&gt; from 4.4 to 10.1 g/kg, depending on the sub-range) than with SLR prediction model (RMSE&lt;sub&gt;test&lt;/sub&gt; from 6.8 to 14 g/kg, depending on the sub-range). Finally, our results showed that the absorbance peak at 2150&amp;#160;cm&lt;sup&gt;-1&lt;/sup&gt; could be used before prediction to separate SIC-poor and SIC-rich test samples (452 and 1726 samples, respectevely). The SLR and PLSR regression methods applied to these SIC-poor and SIC-rich test samples, respectively, provided better prediction performances (&lt;em&gt;R&amp;#178;&lt;/em&gt;&lt;sub&gt;&lt;em&gt;test &lt;/em&gt;&lt;/sub&gt;of 0.95 and &lt;em&gt;RMSE&lt;/em&gt;&lt;sub&gt;&lt;em&gt;test&lt;/em&gt;&lt;/sub&gt; of 3.7 g/kg&lt;sup&gt;&lt;/sup&gt;). &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Finally, this study demonstrated that the use of the spectral absorbance peak at 2150&amp;#160;cm&lt;sup&gt;-1&lt;/sup&gt; provided useful information on Test samples and helped the selection of the optimal prediction model depending on SIC level, when using calibration and test sample sets with very different SIC distributions.&lt;/span&gt;&lt;/p&gt;

scholarly journals Deep subsoiling of a subsurface-compacted typical hapludult under citrus orchard

2013 ◽  
Vol 37 (4) ◽  
pp. 911-919 ◽  
Author(s):  
João Carlos Medeiros ◽  
Getulio Coutinho Figueiredo ◽  
Álvaro Luiz Mafra ◽  
Jaqueline Dalla Rosa ◽  
Sung Won Yoon
Keyword(s):  
Hydraulic Conductivity ◽  
Management Practices ◽  
Soil Management ◽  
Penetration Resistance ◽  
Soil Bulk Density ◽  
Soil Samples ◽  
Fruit Yield ◽  
Root Depth ◽  
Citrus Orchard ◽  
Unsaturated Hydraulic Conductivity

Soil management practices which increase the root depth penetration of citrus are important to the longevity and yield maintenance of this plant, especially in regions where long periods of drought are common, even in soil conventionally subsoiled to a depth of 30-40 cm, when the orchard was first established. The objective of this study was to evaluate the efficiency of subsoiling on the physical and hydric properties of a Typical Hapludult and fruit yield in a 14-year-old citrus orchard located in Piracicaba, SP. The treatments consisted of: no-subsoiling (with no tilling of the soil after the orchard was planted); subsoiling on one side of the plant lines (SUB. 1); and subsoiling on both sides of the plant lines (SUB. 2). The subsoiling treatments were carried out 1.5 m from the plant lines and to a depth of 0.8 m. Soil samples were taken 120 days after this operation, at four depths, in order to determine physical and hydric properties. Fruit yield was evaluated 150 days after subsoiling. Subsoiling between the plant lines of an old established citrus orchard alters the physical and hydric properties of the soil, which is reflected in increased soil macroporosity and unsaturated hydraulic conductivity, and reduced soil bulk density, critical degree-of-compactness and penetration resistance. The improvements in the physical and hydric properties of the soil were related to an increase in fruit number and orchard yield.

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