Results Of Principal Component Analysis Research Articles

Comparative Analysis of Frying Performance: Assessing Stability, Nutritional Value, and Safety of High-Oleic Rapeseed Oils

Frying is a critical process in the food industry, where selecting appropriate vegetable oils is key to achieving optimal results. In this study, French fries were fried at 175 °C with five different oils, the changes in the physicochemical indexes and free radical scavenging rate of the oils during the frying process were investigated, and the most suitable oils for frying were identified through comparative analysis using principal component analysis (PCA). We assessed the frying performances of hot-pressed high-oleic-acid rapeseed oil (HHRO), cold-pressed high-oleic-acid rapeseed oil (CHRO), soybean oil, rice bran oil, and palm oil utilizing principal component analysis over an 18 h period. The HHRO and CHRO showed lower acid values (0.31, 0.26 mg/g), peroxide values (2.09, 1.96 g/100 g), p-anisidine values (152.48, 178.88 g/mL), and total polar compound percentages (27.60%, 32.10%) than other oils. Furthermore, both the HHRO and CHRO demonstrated enhanced free radical scavenging abilities, indicative of their higher antioxidant capacities, as corroborated by the PCA results. Benzopyridine, 3-monochloropropane-1,2-diol ester, squalene, tocopherols, and polyphenol from the HHRO and CHRO during frying were compared. A comprehensive examination of harmful substances versus nutrient retention during frying revealed that the HHRO contained fewer hazardous compounds, while CHRO retained more nutrients. Therefore, this study analyzes the oxidation regulation of HHRO in frying applications, highlights the prospects of HHRO for frying in terms of health and economy, and contributes valuable insights for informed vegetable oil selection within the food industry.

Soil quality assessment in the Araban plain across various land use types

BackgroundSoil is an important resource for improving the quality of human well-being and creating sustainable environmental awareness. Therefore, it is imperative to protect the soil health. This study determined quality of the soils in the Araban plain, Türkiye using three different methods, i.e., principal component analysis (PCA), analytic hierarchy process (AHP), and expert opinion (EO). The major aim was to determine the most appropriate method for determining soil quality and recommending soil protection practices for improving quality. MethodsThis study was conducted on an area of approximately 25,000 ha. A total of 230 soil samples (7 pasture areas, 56 orchards, and 167 arable crops) were analyzed to determine key soil quality indicators. A minimum dataset consisting of parameters that best represent the soils of the region was created. Subsequently, PCA was used to predict soil quality with the optimal accuracy using the least amount of data. The EO was incorporated to validate the PCA results. The AHP was used to assign weights to the parameters. Indicators at all sampling points were converted to unitless scorers. These scores were then formulated as soil quality index (SQI) using the weighted summation method. ResultsOrganic matter (OM) exerted the highest impact on soil quality. Significant variations in SQI values were observed among arable land, orchards land and pastureland (P < 0.01). Significant variations were recorded in SQI values of the assessment methods employed in this study. The lowest (0.552) and highest (0.829) SQI values were recorded for AHP and EO, respectively. Overall, streamlining indicators through PCA gave the most accurate result for SQI value. Subsequently, SQI determination should involve weighing with AHP, particularly towards EO. ConclusionThe AHP was found to be the most appropriate method for evaluating soil quality in Araban. This integrated approach ensures a robust evaluation of soil quality, facilitating more accurate and nuanced insights for informed land management decisions. Overall, 92.61 % of the region’s soil had poor and medium soil quality. Therefore, increasing OM content and aggregate stability are suggested to improve soil quality in the study region.

The analysis of the essential metal contents in khat (Catha edulis Forsk) from Meru County, Kenya

BackgroundChemical composition of plants has attracted considerable interest in recent years with considerable research tailored into determining metal contents in food substances. Particularly, essential elements which are necessary for normal human body functioning and are strongly recommended in optimal proportions for better health. This study determined the levels of essential elements; calcium (Ca), magnesium (Mg), sodium (Na), potassium (K) and manganese (Mn) in eleven khat (Catha edulis Forsk) samples collected from Meru County. Methodology1.0 g of dry ground khat samples were wet-digested in a solvent mixture of 10 mL of HCl, HNO3 and 20 vol of H2O2 for 3 h at variable temperature of 50–90 °C and later reconstituted in 25 mL 0.05 molL−1 HCl before analysis. The samples were analyzed using inductively coupled plasma atomic emission spectrometry (ICP AES) after acid digestion whereas Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used to investigate elemental characteristics and determine the sources of essential metals in khat. In addition, Pearson correlation coefficient (PCC) was used to evaluate the relationships between the essential metals. ResultsSpinach leaves certified reference material (CRM 1750a) was analyzed and the percentage recoveries (%) of 98.4 and 101.7 determined showing that the digestion method employed in this work had adequate accuracy. The validated method was employed for the analysis of all the khat samples. The analyzed metal concentration ranges (mg100g−1) in fresh-weight basis per day of five metals were 160.9–267.3, 44.6–81.7, 2.0–3.0, 237.6–393.6, and 220.3 -349.0 for Ca, Mg, Mn, K, and Na, respectively. Both HCA and PCA results showed that these elements could be linked to natural soils and anthropogenic sources. PCC reported significant correlations between Na and K indicating they may have been derived from the same source. The daily metal intakes were below the World Health Organization (WHO) permissible limits implying no associated health risks for khat users. ConclusionBased on the findings of this study, the consumption of khat from regions of Meru County may be a potential source for essential elements required for human biological processes.

Exploring GIS-Based Modeling for Assessing Social Vulnerability to Ganga Riverbank Erosion, India

Riverside communities along the lower Ganges in India face significant threats like riverbank erosion, floods, and climate change impacts. Despite extensive research on riverbank erosion in the region, a key gap remains in understanding how erosion and climate change jointly affect local communities. Additionally, research prioritizing village-level studies and strategies is urgently needed for effective management of the study area. This study aimed to compute a GIS-based Social Vulnerability Index (SociVI) by assessing 10 components and 31 sub-components at the village level. We used spatial analysis techniques like Moran's I and Getis-Ord G* to identify hotspots and clustering patterns among variables and SociVI values. Principal component analysis (PCA) and multi-correlation statistics determined the most significant component. Our fieldwork involved surveying 1,641 households, 547 focus group discussions, and 12 key informant interviews across 547 villages. The SociVI analysis revealed that residents on the left bank of the river, particularly in the upper section of the Farakka barrage, and those living in the char villages were highly susceptible to social vulnerability. Scores ranged from 0.67 to 0.88, with 34 villages (6.22%) on the left bank and 8 villages (1.46%) on the right bank showing notably high SociVI values. Furthermore, our hot spot analysis identified 51 villages (9.32%) as hot spots with 99% confidence, 7.13% of which were located on the left bank and 2.19% on the right bank. According to the PCA results, demographics (PC1), riverbank calamities (PC2), displacement of households (PC3), and climatic variability (PC4) emerged as the most significant factors. This study's findings are crucial, highlighting critical areas and villages requiring focused efforts to reduce local vulnerability and bolster adaptation capacities amid these challenges.

A comprehensive and innovative chemometric approach: Archaeometric analysis of the sherds from the Neolithic Period to the Chalcolithic and early Bronze Age with the full deployment of FTIR's molecular spectroscopic capabilities

In this study, the mineralogical composition of 284 sherds obtained from the sites of Yeşilova Höyük and Yassıtepe Höyük, two of the oldest settlements in Western Anatolia, and corresponding to a wide period starting from the Neolithic period to the Chalcolithic and Early Bronze Age (EBA), was studied by attenuated total reflectance (ATR) Fourier transform infrared (FTIR) spectroscopy. FTIR data was used to classify the sherds and state their firing conditions as a result of determining their mineralogical composition using principal component analysis (PCA), multivariate curve resolution (MCR), and hierarchical cluster analysis (HCA). According to the PCA results, the second-order derivative FTIR spectrum with 19 smoothing points in the range of 1300–400 cm−1 was determined to be the most successful model in classifying sherds. As a result of MCR, which allows comparison of standard minerals obtained within the scope of the study, it was determined that kaolinite, illite, chlorite, and some feldspar types were dominant in the sherds. According to HCA analysis, all sherds except five of them exhibited a similar mineralogical structure. According to the derivative spectra, it was seen that the sherds had a composition consisting of kaolinite, illite, hematite, chlorite, and some feldspar types at different rates. Upon examining the data, it becomes evident that the sherds are composed of nearly identical minerals and might have been fired in an oxidizing atmosphere. Even while 279 sherds had many mineralogical traits, N-061, EB-030, N-043, C-068, and N-059 were found to have distinct proportions of comparable minerals, including more kaolinite, illite, calcite, and chlorite. Regarding color, additives, and the amount of ceramic pastes present, these five sherds differ from the others. This suggests that there might have been attempts at manufacture or pots brought in from outside the Yeşilova Höyük-Yassıtepe Höyük sites. The results provided from the derivative spectra showed that all sherds except five of them have been fired at about at temperatures slightly above 450–500 °C due to their relatively low kaolinite character, but below 800 °C because diopside and similar high-temperature minerals could not be detected. The five sherds may have been fired at a temperature of around 450–500 °C due to the presence of a high character of kaolinite, illite, and chlorite. It was understood that the residents of the sites of Yeşilova Höyük and Yassıtepe Höyük produced their ceramics using the same raw materials and the same production methods from the Neolithic period to EBA thanks to FTIR and chemometrics, which are very good tools for analyzing large numbers of sherds with low cost and fast analysis time.

Design E-Learning User Interface On Website-Based Edspert.Id With Kansei Engineering Methods

The development of information technology has encouraged people to rely on information systems, especially through websites. Websites provide easy access to information and learning with various educational materials. Although e-learning has been implemented in many educational websites, Edspert. id, a company in the education sector, has not implemented it yet. User interface design development is one of the important processes in e-learning website development. A user interface that is easy to use will improve the learning experience of learners. This research proposes a solution to design the user interface of Edspert.id e-learning website by using the Kansei Engineering method. This approach has been done beforefor web-based e-learning based on users' emotions. Principal component analysis (PCA) is used to reduce Kansei Word variables that are relevant to users' emotions. The e-learning website element design was then designed based on the PCA results. The next step is to determine the design elements in the e-learning design. Then, partial Least Square (PLS) was used to analyze the relationship between Kansei Word and element design. The results show that there multiuser interface design has two concepts whose element designs are in accordance with user needs.

Origin of spring water chemistry and its appropriateness for drinking requirements in a semi-arid environment (Ain Larbi, Algeria)

ABSTRACT A comparative assessment of water quality for drinking purposes was conducted in Ain Larbi area using the Weighted Arithmetic Water Quality Index (WA-WQI). The origin of minerals is assessed by a saturation index approach and the Gibbs diagram. Also, mineral correlation process and ion exchange mechanism are detected by the Principal Component Analysis (PCA). The PCA results indicate that the groundwater quality in the Ain Larbi study area primarily results from weathering and geochemical alteration of rock. The saturation index and Gibbs diagram support these findings. The WA-WQI was calculated for each station based on physicochemical parameters. During the wet season, three out of five classes of WQI were observed in the present study, including excellent (25%), good (70%), and poor (5%). In the dry season, the classes excellent (25%), good (30%), and poor (45%) were represented. The present situation demonstrates how hydrogeological regimes and water–rock interactions affect groundwater ion concentrations. Our results contribute to identifying drinking water requirements in the Ain Larbi region, promoting sustainable water management.

Analysis of lard in cod liver oil emulsion using FTIR spectroscopy combined principal component analysis

Cod liver oil (CLO) contains essential fatty acids and has a high price in the market. Therefore, CLO can be targeted for adulteration to increase economic profits. One of the substances used for adulteration of CLO is lard. This study aimed to analyze the spectral profile of lard and CLO in cod liver oil emulsion using Fourier transform infrared (FTIR) spectroscopy combined with principal component analysis (PCA). Lard and CLO were mixed with a range of concentrations of 0-100% used as the control. CLO emulsions were made with a composition of lard and CLO in concentrations similar to those of the control. To analyze the lard and CLO spectrum, lard and CLO were extracted from the emulsion using liquid-liquid extraction, and then the oil extracted from the emulsion was analyzed using FTIR spectroscopy and PCA. Fourier transform infrared spectrum absorption at region 1117–1098 cm-1 is specific to identifying the different spectrums of CLO and lard. The results of PCA showed that clustering between similar oil content and a combination of FTIR and PCA is effective in distinguishing the spectrum of lard and CLO in cod liver oil emulsion.

Geochemistry of Cenozoic coals from Sarawak Basin, Malaysia: implications for paleoclimate, depositional conditions, and controls on petroleum potential

Forty Tertiary coals from Mukah-Balingian and Merit-Pila coalfields of the Sarawak Basin, Malaysia were investigated using bulk and molecular geochemical techniques such as proximate analysis, gas chromatography-mass spectrometry, elemental analyser, isotope ratio mass spectrometry, and inductively coupled plasma mass spectrometry to reconstruct their paleovegetation, paleoclimate, and environments of deposition. In addition, principal component analysis (PCA) of selected geochemical parameters was carried out to determine the controlling influences on the petroleum potential of the humic coals. δ13C values and the abundance of terpenoids imply the predominant contribution of angiosperms to the paleoflora. Bimetal proxies (Sr/Ba, Sr/Cu, and C-value), and δD values are generally suggestive of a warm and humid climate during the accumulation of the paleopeats. However, n-alkane proxies (Pwax, Paq, n-C23/n-C29, etc.) and polycyclic aromatic hydrocarbons (PAHs) distribution suggest that Balingian coals accumulated under relatively drier and strongly seasonal paleoclimate in the Late Pliocene. When compared with published global average abundances, the investigated coals are mostly depleted in major oxides and trace elements, suggesting peat accumulation in freshwater-influenced environments. Nonetheless, higher (> 0.5 wt%) total sulfur content in some Mukah-Balingian coals suggests some degree of epigenetic marine influence. Furthermore, the low to moderately-high ash contents of the Sarawak Basin coals indicate the presence of ombrotrophic and rheotrophic peat deposits. PCA result of selected geochemical proxies suggests that source input, paleoflora, and marine incursions are not major controlling influences on the petroleum potential. However, climatic, and depositional conditions appear to slightly influence the petroleum potential of the studied humic coals.

Coastline Automatic Extraction from Medium-Resolution Satellite Images Using Principal Component Analysis (PCA)-Based Approach

In recent decades several methods have been developed to extract coastlines from remotely sensed images. In fact, this is one of the principal fields of remote sensing research that continues to receive attention, as testified by the thousands of scientific articles present in the main databases, such as SCOPUS, WoS, etc. The main issue is to automatize the whole process or at least a great part of it, so as to minimize the human error connected to photointerpretation and identification of training sites to support the classification of objects (basically soil and water) present in the observed scene. This article proposes a new fully automatic methodological approach for coastline extraction: it is based on the unsupervised classification of the most decorrelated fictitious band derived from Principal Component Analysis (PCA) applied to the satellite images. The experiments are carried out on datasets characterized by images with different geometric resolution, i.e., Landsat 9 Operational Land Imager (OLI) multispectral images (pixel size: 30 m), a Sentinel-2 dataset including blue, green, red and Near Infrared (NIR) bands (pixel size: 10 m) and a Sentinel-2 dataset including red edge, narrow NIR and Short-Wave Infrared (SWIR) bands (pixel size: 20 m). The results are very encouraging, given that the comparison between each extracted coastline and the corresponding real one generates, in all cases, residues that present a Root Mean Squared Error (RMSE) lower than the pixel size of the considered dataset. In addition, the PCA results are better than those achieved with Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) applications.

Quality markers of Polygala fallax Hemsl decoction based on qualitative and quantitative analysis combined with network pharmacology and chemometric analysis.

Polygala fallax Hemsl (PFH) is a widely used herbal medicine in Guangxi, China. At present, research on PFH mainly focuses on extraction technology and cultivation, lacking quality control standards for systematic evaluation. The study aimed to assess the quality of PFH from different sources and to predict markers that would help assess quality. Fingerprinting of 15 batches of PFH samples was performed by ultra-high performance liquid chromatography (UPLC) and similarity was assessed using hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares discrimination (OPLS-DA). Differential components were screened by mathematical analysis, and a "component-target-pathway" network map was constructed in combination with network pharmacology, quality markers (Q-markers) of PFH were predicted, and quantitative analysis was performed. Fifteen batches were fingerprinted for PFH, with 11 common peaks, and peak 5 was identified as 4-hydroxybenzoic acid, which was generally consistent with the results of HCA, PCA, and OPLS-DA. Network pharmacology screened 18 potential compounds, 45 core targets, and 20 key pathways, integrating fingerprinting, pattern recognition, and network pharmacology methods. One of the potential Q-markers that can identify the principle of testability, efficacy, and specificity is 4-hydroxybenzoic acid, whose content ranges from 0.0188 to 1.4517 mg/g. The potential Q-markers of PFH were predicted by integrating fingerprinting, pattern recognition, and network pharmacological analysis, which provided a scientific basis for the overall control and evaluation of the quality of PFH and a theoretical reference for the study of the quality standard of multi-base traditional Chinese medicine.

VCF2PCACluster: a simple, fast and memory-efficient tool forprincipal component analysis of tens of millions of SNPs.

Principal component analysis (PCA) is an important and widely used unsupervised learning method that determines population structure based on genetic variation. Genome sequencing of thousands of individuals usually generate tens of millions of SNPs, making it challenging for PCA analysis and interpretation. Here we present VCF2PCACluster, a simple, fast and memory-efficient tool for Kinship estimation, PCA and clustering analysis, and visualization based on VCF formatted SNPs. We implemented five Kinship estimation methods and three clustering methods for its users to choose from. Moreover, unlike other PCA tools, VCF2PCACluster possesses a clustering function based on PCA result, which enabling users to automatically and clearly know about population structure. We demonstrated the same accuracy but a higher performance of this tool in performing PCA analysis on tens of millions of SNPs compared to another popular PLINK2 software, especially in peak memory usage that is independent of the number of SNPs in VCF2PCACluster.

Description of The Phenotypic Characteristics of Some Tomato Genotypes

This study was carried out in order to determine some morphological characteristics of 240 tomato genotypes at S2 level and to demonstrate the usability of these materials in breeding programs. Accordingly, the average fruit weight was between 553.11 gr and 74.83 gr. The mean pericarp thickness, carpel number, and soluble dry matter content of the genotypes were 7.38 mm, 3.6 pieces and 4.04% brix, respectively. Based on these measurements and observations, tomato genotypes were also investigated using the Cluster and Principal Component Analysis (PCA) methods. According to the PCA results, the first component explained 24.1% of the total variance, and the number of fruit carpels, average fruit weight, and fruit width were the most highly explained parameters. The second and third main components constituted 13.06% and 11.16% of the total. Based on the results of fruit characterization studies, genotypes 233, 447, 126, 22, 391, 118, 71, 100, 340, 102 were determined as superior. Overall, A high degree of morphological variation was detected among the characterized tomato genotypes.

Pollution Characteristics and Risk Assessment of Heavy Metals in the Sediments of the Inflow Rivers of Dianchi Lake, China.

To explore the contamination status and identify the source of the heavy metals in the sediments in the major inflow rivers of Dianchi Lake in China, sediment samples were collected and analyzed. Specifically, the distribution, source, water quality, and health risk assessment of the heavy metals were analyzed using correlation analysis (CA), principal component analysis (PCA), the heavy metal contamination factor (Cf), the pollution load index (PLI), and the potential ecological risk index (PERI). Additionally, the chemical fractions were analyzed for mobility characteristics. The results indicate that the average concentration of the heavy metals in the sediment ranked in the descending order of Zn > Cr > Cu > Pb > As > Ni > Cd > Hg, and most of the elements existed in less-mobile forms. The Cfwas in the order of Hg > Zn > Cd > As > Pb > Cr > Ni; the accumulation of Hg, Zn, Cd, and As was obvious. Although the spatial variability of the heavy metal contents was pronounced, the synthetical evaluation index of the PLI and PERI both reached a high pollution level. The PCA and CA results indicate that industrial, transportation, and agricultural emissions were the dominant factors causing heavy metal pollution. These results provide important data for improving water resource management efficiency and heavy metal pollution prevention in Dianchi Lake.

What makes neighbourhood-level commercial centres attractive for neighbourhood residents?

ABSTRACT Neighbourhood commercial centres (NCC) are dynamic hubs that provide essential goods, promote community bonds and drive economic growth. Despite numerous studies on shopping behaviour, there remains a notable gap in understanding how transportation and shopping centre-related factors collectively shape the attractiveness of the NCCs, especially in developing countries like India. This research aims to understand why some NCCs outperform others in terms of attractiveness. This study proposes the neighbourhood commercial centre attractiveness index (NCCAI), which is developed by collecting 455 samples from 13 NCCs with the help of a primary survey to understand how different factors influence the attractiveness of NCCs in the Indian planned city context. The study used principal component analysis (PCA) and identified five major components that significantly contribute to NCC’s attractiveness. Further, the weightage of the components was determined through two distinct approaches: one based on PCA results and the other utilising the analytic hierarchy process (AHP) with expert opinion data. Finally, the more reliable weights from these two methodologies were selected using analysis of covariance (ANCOVA). The NCCAI can help urban planners, store owners and retail managers, providing data-driven insights to enhance overall NCC attractiveness. The decomposable nature of NCCAI allows for component score breakdown, enabling planners and policymakers to allocate resources strategically.

The Effect of Fermentation on the Chemical Constituents of Gastrodia Tuber Hallimasch Powder (GTHP) Estimated by UHPLC-Q-Orbitrap HRMS and HPLC.

To compare the effect of fermentation on the chemical constituents of Gastrodia Tuder Halimasch Powder (GTHP), to establish its fingerprinting and multicomponent content determination, and to provide a basis for the processing, handling, and clinical application of this herb. Ultra-high-performance liquid chromatography-quadrupole-Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) was used to conduct a preliminary analysis of the chemical constituents in GTHP before and after fermentation. High-performance liquid chromatography (HPLC) was used to determine some major differential components of GTHP and establish fingerprints. Cluster analysis (CA), and principal component analysis (PCA) were employed for comprehensive evaluation. Seventy-nine compounds were identified, including flavonoids, organic acids, nucleosides, terpenoids, and others. The CA and PCA results showed that ten samples were divided into three groups. Through standard control and HPLC analysis, 10 compounds were identified from 22 peaks, namely uracil, guanosine, adenosine, 5-hydroxymethylfurfural (5-HMF), daidzin, genistin, glycitein, daidzein, genistein, and ergosterol. After fermentation, GTHP exhibited significantly higher contents of uracil, guanosine, adenosine, 5-hydroxymethylfurfural, and ergosterol and significantly lower genistein and daidzein contents. The UHPLC-Q-Orbitrap HRMS and HPLC methods can effectively identify a variety of chemical components before and after the fermentation of GTHP. This study provides a valuable reference for further research on the rational clinical application and quality control improvement of GTHP.

The fruit quality and nutrient content of kiwifruit produced by organic versus chemical fertilizers.

Currently, organic farming has become a feasible approach for the production of high-quality fruits. To evaluate the response of fruit quality and mineral nutrition contents of Hayward Kiwifruit affected by different organic and inorganic fertilizers, the present study was conducted in Citrus and Subtropical Fruits Research Center, Iran, in 2017-2021, as a randomized block design with three replications. The studied treatments were organic fertilizers (cow, vermicompost and Azolla) and chemical fertilizers. After 4 years of fertilization, the fruit's nutritional elements content and some fruit bioactive compounds were evaluated after 3 months of cold storage and then analyzed by the principal component analysis (PCA) method. The use of organic amendments boosted the calcium, phosphorus, potassium and iron content of the kiwifruits compared to chemical fertilizers. The highest fruit potassium and phosphorus content were recorded in the cow manure treatment. The lowest amount of nitrate and the highest calcium, zinc, copper and manganese accumulation were recorded in the fruits treated with vermicompost. In addition to mineral nutrients, the dry matter, total soluble solids, total phenolic and antioxidant capacity of kiwifruit were improved by the application of vermicompost amendment compared to the other fertilizer sources. However, the highest fruit vitamin C and total soluble carbohydrates were measured in the cow manure treatment. The PCA results of the fruit quality indices indicated that fertilization treatments were ranked as vermicompost (1.88) > cow manure (1.63) = chemical (1.60) > Azolla (1.54). It is concluded that the application of 40 kg of vermicompost or 40 kg of cow manure in the next rank in Hayward kiwifruit orchards in March (growth stage beginning of bud swelling) may be a more suitable approach for improving the nutritional quality of the fruit. © 2024 Society of Chemical Industry.

Electronic nose for detecting Colletotrichum coccodes causing anthracnose fruit rots in tomatoes

Electronic nose (EN) technology has several applications targeting various study areas, and food spoilage is one of these sections. This study investigated the ability of the EN, with metal-oxide sensors (MOS), to sense the presence of Colletotrichum coccodes, which cause anthracnose fruit rots, on tomato fruit and to distinguish between different stages and doses of infection. C. coccodes were identified by an appropriate polymerase chain reaction (PCR) program and sequencing, using two different primer sets: ITS1F/ITS4 and Cc1F1/Cc2R1. Three intact homogeneous tomato samples were injected with three different concentrations of conidial fungal suspension: 3 × 104, 3 × 105 and 3 × 106 conidia/ml, and three others were kept as negative controls. Principal component analysis (PCA) was used in analyzing the data acquired from the EN sensors. The results of PCA showed that EN was able to detect the presence of C. coccodes pathogen at early stages (two days of infection and before any symptoms appear). Moreover, EN was able to differentiate between different infection stages. In addition, the concentrations of infected samples on the sixth, eighth and tenth days were clearly differentiated. The use of EN is a promising technique for determining quality assurance and control.

142 Validation of the Mentoring Competency Assessment to evaluate the mentorship skills and competencies of mentees

OBJECTIVES/GOALS: The purpose of this study was to assess if the Mentoring Competency Assessment (MCA) could be used to capture mentee gains in mentorship skills and how the mentorship competencies may vary structurally for mentees compared to mentors, while the original MCA was shown to be a validated measure to assess mentor skills. METHODS/STUDY POPULATION: The mentee training survey data were collected nationally from 2015 to 2022. The survey data set included 401 respondents who consented to participate after 59 mentee training events hosted by 34 institutions/organizations who participated in face-to-face and online training as well as completed the Mentoring Competency Assessment (MCA) in their surveys. We conducted principal component analysis (PCA) with varimax rotation to investigate the internal structure of the MCA and Hatcher’s criteria were applied. After a team of mentoring experts independently interpreted the PCA results and reached a consensus on the interpretations of the components, factor analysis and internal consistency reliability analysis were applied to assess the construct validity and the reliability. RESULTS/ANTICIPATED RESULTS: There were significant component loadings of the eight components with varimax rotation and 22 of the total 26 items were loaded into components. Four items, (5) pursuing strategies to improve communication, (6) coordinating with other mentors, (11) developing strategies to meet goals, and (23) setting career goals, were excluded from the factor analysis and Cronbach’s alpha analysis since these items were not significantly loaded into any components. The eight-component structure was validated (χ2=313.209, p&lt;.001, RMSEA=.083, CFI=.907, TLI=.881, SRMR=.073) and the hypothesized model of the eight components resulted in an acceptable fit to the data with standardized factor loadings ranging from 0.58 to 0.93. The alpha coefficient is from 0.58 to 0.90, suggesting the items have high internal consistency. DISCUSSION/SIGNIFICANCE: Based upon the findings we recommend that the full revised MCA for mentees is used to capture mentees’ mentorship skill gains even if not all of the competency modules are used in the training. The development and validation of measures such as the MCA are important as we move toward the use of common measures across programs such as the CTSAs.

Data-Driven Classification and Logging Prediction of Mudrock Lithofacies Using Machine Learning: Shale Oil Reservoirs in the Eocene Shahejie Formation, Bonan Sag, Bohai Bay Basin, Eastern China

As the world’s energy demand continues to expand, shale oil has a substantial influence on the global energy reserves. The third submember of the Mbr 3 of the Shahejie Fm, characterized by complicated mudrock lithofacies, is one of the significant shale oil enrichment intervals of the Bohai Bay Basin. The classification and identification of lithofacies are key to shale oil exploration and development. However, the efficiency and reliability of lithofacies identification results can be compromised by qualitative classification resulting from an incomplete workflow. To address this issue, a comprehensive technical workflow for mudrock lithofacies classification and logging prediction was designed based on machine learning. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were conducted to realize the automatic classification of lithofacies, which can classify according to the internal relationship of the data without the disturbance of human factors and provide an accurate lithofacies result in a much shorter time. The PCA and HCA results showed that the third submember can be split into five lithofacies: massive argillaceous limestone lithofacies (MAL), laminated calcareous claystone lithofacies (LCC), intermittent lamellar argillaceous limestone lithofacies (ILAL), continuous lamellar argillaceous limestone lithofacies (CLAL), and laminated mixed shale lithofacies (LMS). Then, random forest (RF) was performed to establish the identification model for each of the lithofacies and the obtained model is optimized by grid search (GS) and K-fold cross validation (KCV), which could then be used to predict the lithofacies of the non-coring section, and the three validation methods showed that the accuracy of the GS–KCV–RF model were all above 93%. It is possible to further enhance the performance of the models by resampling, incorporating domain knowledge, and utilizing the mechanism of attention. Our method solves the problems of the subjective and time-consuming manual interpretation of lithofacies classification and the insufficient generalization ability of machine-learning methods in the previous works on lithofacies prediction research, and the accuracy of the model for mudrocks lithofacies prediction is also greatly improved. The lithofacies machine-learning workflow introduced in this study has the potential to be applied in the Bohai Bay Basin and comparable reservoirs to enhance exploration efficiency and reduce economic costs.