scholarly journals Correction of Temperature Variation with Independent Water Samples to Predict Soluble Solids Content of Kiwifruit Juice Using NIR Spectroscopy

Molecules ◽  
2022 ◽  
Vol 27 (2) ◽  
pp. 504
Author(s):  
Harpreet Kaur ◽  
Rainer Künnemeyer ◽  
Andrew McGlone
Keyword(s):  
Temperature Variation ◽  
Water Samples ◽  
Pure Water ◽  
Partial Least Square ◽  
Least Square ◽  
Soluble Solids ◽  
External Parameter ◽  
Wavelength Shift ◽  
Data Set ◽  
Prediction Bias

Using the framework of aquaphotomics, we have sought to understand the changes within the water structure of kiwifruit juice occurring with changes in temperature. The study focuses on the first (1300–1600 nm) and second (870–1100 nm) overtone regions of the OH stretch of water and examines temperature differences between 20, 25, and 30 °C. Spectral data were collected using a Fourier transform–near-infrared spectrometer with 1 mm and 10 mm transmission cells for measurements in the first and second overtone region, respectively. Water wavelengths affected by temperature variation were identified. Aquagrams (water spectral patterns) highlight slightly different responses in the first and second overtone regions. The influence of increasing temperature on the peak absorbance of the juice was largely a lateral wavelength shift in the first overtone region and a vertical amplitude shift in the second overtone region of water. With the same data set, we investigated the use of external parameter orthogonalisation (EPO) and extended multiple scatter correction (EMSC) pre-processing to assist in building temperature-independent partial least square regression models for predicting soluble solids concentration (SSC) of kiwifruit juice. The interference component selected for correction was the first principal component loading measured using pure water samples taken at the same three temperatures (20, 25, and 30 °C). The results show that the EMSC method reduced SSC prediction bias from 0.77 to 0.1 °Brix in the first overtone region of water. Using the EPO method significantly reduced the prediction bias from 0.51 to 0.04 °Brix, when applying a model made at one temperature (30 °C) to measurements made at another temperature (20 °C) in the second overtone region of water.

From managing customers to joint venturing with customers: co-creating service value in the digital age

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Christine Falkenreck ◽  
Ralf Wagner
Keyword(s):  
Perceived Risk ◽  
Structural Equation ◽  
Business Models ◽  
Joint Venture ◽  
Digital Age ◽  
Partial Least Square ◽  
Least Square ◽  
Equation Modeling ◽  
Data Set ◽  
Content Type

Purpose Until today, scholars claim that the phenomenon of “co-creation” of value in an “interacted” economy and in the context of positive actor-to-actor relationships has not been adequately explored. This study aims to first to identify and separate the accessible values of internet of things (IoT)-based business models for business-to-business (B2B) and business-to-government (B2G) customer groups. It quantifies the drivers to successfully implement disruptive business models. Design/methodology/approach Data were gathered from 292 customers in Western Europe. The conceptual framework was tested using partial least square structural equation modeling. Findings Managing disruptions in the digital age is closely related to the fact that the existing trust in buyer-seller relationships is not enough to accept IoT projects. A company’s digitalization capabilities, satisfaction with the existing relationship and trust in the IoT credibility of the manufacturer drives the perceived value of IoT-based business models in B2B settings. Contrastingly, in B2G settings, money is less important. Research limitations/implications Research refers to one business field, the data set is of European origin only. Findings indicate that the drivers to engage in IoT-related projects differ significantly between the customer groups and therefore require different marketing management strategies. Saving time today is more important to B2G buyers than saving money. Practical implications The disparate nature of B2B and B2G buyers indicates that market segmentation and targeted marketing must be considered before joint-venturing in IoT business models. To joint venture supply chain partners co-creating value in the context of IoT-related business models, relationship management should be focused with buyers on the same footing, as active players and co-developers of a personalized experience in digital service projects. Originality/value Diverging from established studies focusing on the relationship within a network of actors, this study defines disruptive business models and identifies its drivers in B2B and B2G relationships. This study proposes joint venturing with B2B and B2G customers to overcome the perceived risk of these IoT-related business models. Including customers in platforms and networks may lead to the co-creation of value in joint IoT projects.

scholarly journals Entrepreneurial self-efficacy and entrepreneurial intention: The mediating role of the need for independence

2021 ◽  
Vol 17 (4) ◽  
pp. 91-119
Author(s):  
Victor Osadolor ◽  
◽  
Kalu Emmanuel Agbaeze ◽  
Ejikeme Emmanuel Isichei ◽  
Samuel Taiwo Olabosinde ◽  
...  
Keyword(s):  
Self Efficacy ◽  
Structural Equation ◽  
Entrepreneurial Intention ◽  
Partial Least Square ◽  
Least Square ◽  
Data Set ◽  
Behavioral Reasoning Theory ◽  
The Relationship ◽  
Behavioral Reasoning

PURPOSE: The paper focuses on assessing the direct effect of entrepreneurial self-efficacy and entrepreneurial intention and the indirect effect of the need for independence on the relationship between the constructs. Despite increased efforts towards steering the interest of young graduates towards entrepreneurial venture, the response rate has been rather unimpressive and discouraging, thus demanding the need to account for what factors could drive intention towards venture ownership among graduates in Nigeria. METHODOLOGY: A quantitative approach was adopted and a data set from 235 graduates was used for the study. The data was analyzed using the partial least square structural equation model (PLS-SEM). FINDINGS: It was found that self-efficacy does not significantly affect intention. It was also found that the need for independence affects entrepreneurial intention. The study found that the need for independence fully mediates the relationship between entrepreneurial self-efficacy and entrepreneurial intention. PRACTICAL IMPLICATIONS: This paper provides new insight into the behavioral reasoning theory, through its application in explaining the cognitive role of the need for independence in decision-making, using samples from a developing economy. ORIGINALITY AND VALUE: The study advances a new perspective on the underlining factors that account for an entrepreneur’s intent to start a business venture, most especially among young graduates in Nigeria, through the lens of the behavioral reasoning theory. We further support the application of the theory in entrepreneurship literature, given the paucity of studies that have adopted the theory despite its relevance.

Factors that trigger bullying amongst subcontractors toward intention to quit in the construction projects

2019 ◽  
Vol 10 (1) ◽  
pp. 140-152
Author(s):  
Alireza Jalali ◽  
Nur Izzati Hidzir ◽  
Mastura Jaafar ◽  
Norziani Dahalan
Keyword(s):  
Construction Projects ◽  
Workplace Bullying ◽  
Construction Project ◽  
Partial Least Square ◽  
Least Square ◽  
Intention To Quit ◽  
Equation Modeling ◽  
Key Factors ◽  
Data Set ◽  
Content Type

Purpose The purpose of this paper is to examine the relationships between three key factors that cause workplace bullying among subcontractor managers toward intention to quit the undertaken project within the context of Malaysia. Design/methodology/approach This study utilized the simple sampling method to select its study sample, while the questionnaire survey approach was implemented amidst 500 G6 and G7 contractor managers across Peninsular Malaysia. A total of 210 completed questionnaires were returned. Partial least square-structural equation modeling was administered to analyze the data via SmartPls 3.0 software. Findings This study discovered three significant factors (main contractor leadership, construction culture, work organization and job design) that displayed positive effect on workplace bullying among subcontractor managers toward intention to quit. The study outcomes can serve as a direction for policy makers to reduce bullying within the construction project environment. Practical implications This study serves as an instruction for main contractors to reinvent their style of management in overcoming bullying in construction projects. This paper guides that collaborative relationship among various parties in construction projects, including the representatives of main contractors and subcontractor managers, may assist in addressing the hostile environment of construction project, in order to create a constructive relationship between them that leads to overall project success. Originality/value Recognition of the three key factors that lead to workplace bullying among subcontractor managers in the construction industry, which are bound to enhance intention to quit based on the data set with strong statistical results, has made the research original.

scholarly journals Understanding critically ill sepsis patients with normal serum lactate levels: results from U.S. and European ICU cohorts

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Christopher M. Sauer ◽  
Josep Gómez ◽  
Manuel Ruiz Botella ◽  
David R. Ziehr ◽  
William M. Oldham ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Critically Ill ◽  
Serum Lactate ◽  
High Serum ◽  
Normal Serum ◽  
Partial Least Square ◽  
Least Square ◽  
Data Set ◽  
Severity Scores ◽  
Lactate Levels

AbstractWhile serum lactate level is a predictor of poor clinical outcomes among critically ill patients with sepsis, many have normal serum lactate. A better understanding of this discordance may help differentiate sepsis phenotypes and offer clues to sepsis pathophysiology. Three intensive care unit datasets were utilized. Adult sepsis patients in the highest quartile of illness severity scores were identified. Logistic regression, random forests, and partial least square models were built for each data set. Features differentiating patients with normal/high serum lactate on day 1 were reported. To exclude that differences between the groups were due to potential confounding by pre-resuscitation hyperlactatemia, the analyses were repeated for day 2. Of 4861 patients included, 47% had normal lactate levels. Patients with normal serum lactate levels had lower 28-day mortality rates than those with high lactate levels (17% versus 40%) despite comparable physiologic phenotypes. While performance varied between datasets, logistic regression consistently performed best (area under the receiver operator curve 87–99%). The variables most strongly associated with normal serum lactate were serum bicarbonate, chloride, and pulmonary disease, while serum sodium, AST and liver disease were associated with high serum lactate. Future studies should confirm these findings and establish the underlying pathophysiological mechanisms, thus disentangling association and causation.

Corn microbial diversity and its relationship to yield

2020 ◽  
Vol 66 (8) ◽  
pp. 457-473
Author(s):  
Shimaila Ali ◽  
Soledad Saldias ◽  
Nimalka Weerasuriya ◽  
Kristen Delaney ◽  
Saveetha Kandasamy ◽  
...  
Keyword(s):  
Restriction Fragment ◽  
Binary Data ◽  
Bacterial Population ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Reference Database ◽  
Corn Yield ◽  
Data Set ◽  
Highly Correlated

This study aimed to identify possible relationships between corn (Zea mays L.) productivity and its endosphere microbial community. Any insights would be used to develop testable hypotheses at the farm level. Sap was collected from 14 fields in 2014 and 10 fields in 2017, with a yield range of 10.1 to 21.7 tonnes per hectare (t/ha). The microbial sap communities were analyzed using terminal restriction fragment length polymorphism (TRFLP) and identified using an internal pure culture reference database and BLAST. This technique is rapid and inexpensive and is suitable for use at the grower level. Diversity, richness, and normalized abundances of each bacterial population in corn sap samples were evaluated to link the microbiome of a specific field to its yield. A negative trend was observed (r = –0.60), with higher-yielding fields having lower terminal restriction fragment (TRF) richness. A partial least square regression analysis of TRF intensity and binary data from 2014 identified 10 TRFs (bacterial genera) that positively, or negatively, correlated with corn yields, when either absent or present at certain levels or ratios. Using these observations, a model was developed that accommodated criteria for each of the 10 microbes and assigned a score for each field out of 10. Data collected in 2014 showed that sites with higher model scores were highly correlated with larger yields (r = 0.83). This correlation was also seen when the 2017 data set was used (r = 0.87). We were able to conclude that a positive significant effect was seen with the model score and yield (adjusted R2 = 0.67, F[1,22] = 46.7, p < 0.001) when combining 2014 and 2017 data. The results of this study are being expanded to identify the key microbes in the corn sap community that potentially impact corn yield, regardless of corn variety, geographic factors, or edaphic factors.

Influence of Sampling Component on Determination of Soluble Solids Content of Fuji Apple Using Near-Infrared Spectroscopy

2016 ◽  
Vol 71 (5) ◽  
pp. 856-865 ◽  
Author(s):  
Shuye Qi ◽  
Seiichi Oshita ◽  
Yoshio Makino ◽  
Donghai Han
Keyword(s):  
Near Infrared ◽  
Visible Region ◽  
Partial Least Square ◽  
Least Square ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Quantitative Accuracy ◽  
Interference Signals ◽  
Solids Content ◽  
Production Areas

Fuji apples from two production areas were separated into six batches by different experimenters. After applying light (500–1010 nm) on the surface of intact ones for their visible and near-infrared (NIR) spectra, destructive samples of three apple components were taken to determine the soluble solids content (SSC). Correlation and regression coefficients between the second Savitzky–Golay derivative of the spectra and SSC were analyzed to reveal that SSC values derived from the different apple components showed significantly different responses in the visible region. However, similar responses, particularly in the NIR section (730–932 nm), remained, including two sugar bands at 890 and 906 nm. On the basis of applying above characteristic bands to remove the interference signals, partial least square (PLS) and multiple linear regression (MLR) showed similar effective performances. According to the analysis of variance (ANOVA) method, sampling methods had significant effect on quantitative accuracy, and the model, using SSC values detected from the outer flesh cuboid (2.5 × 2.5 × 1.5 cm3), provided the best performance with lower root mean square error of prediction and higher correlation coefficient.

scholarly journals Comprehensive ripeness-index for prediction of ripening level in mangoes by multivariate modelling of ripening behaviour

2017 ◽  
Vol 31 (1) ◽  
pp. 35-44 ◽  
Author(s):  
Vijayaram Eyarkai Nambi ◽  
Kuladaisamy Thangavel ◽  
Annamalai Manickavasagan ◽  
Sultan Shahir
Keyword(s):  
Linear Regression ◽  
Principal Component ◽  
Textural Properties ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Soluble Solids ◽  
Variable Model ◽  
Multi Linear Regression ◽  
Ripeness Index

Abstract Prediction of ripeness level in climacteric fruits is essential for post-harvest handling. An index capable of predicting ripening level with minimum inputs would be highly beneficial to the handlers, processors and researchers in fruit industry. A study was conducted with Indian mango cultivars to develop a ripeness index and associated model. Changes in physicochemical, colour and textural properties were measured throughout the ripening period and the period was classified into five stages (unripe, early ripe, partially ripe, ripe and over ripe). Multivariate regression techniques like partial least square regression, principal component regression and multi linear regression were compared and evaluated for its prediction. Multi linear regression model with 12 parameters was found more suitable in ripening prediction. Scientific variable reduction method was adopted to simplify the developed model. Better prediction was achieved with either 2 or 3 variables (total soluble solids, colour and acidity). Cross validation was done to increase the robustness and it was found that proposed ripening index was more effective in prediction of ripening stages. Three-variable model would be suitable for commercial applications where reasonable accuracies are sufficient. However, 12-variable model can be used to obtain more precise results in research and development applications.

scholarly journals Non-Destructive Quality Assessment of Tomato Paste by Using Portable Mid-Infrared Spectroscopy and Multivariate Analysis

Foods ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1300
Author(s):  
Didem Peren Aykas ◽  
Karla Rodrigues Borba ◽  
Luis E. Rodriguez-Saona
Keyword(s):  
External Validation ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Soluble Solids ◽  
Quality Traits ◽  
Sampling System ◽  
Mid Infrared ◽  
Tomato Paste ◽  
Ft Ir

This research aims to provide simultaneous predictions of tomato paste’s multiple quality traits without any sample preparation by using a field-deployable portable infrared spectrometer. A total of 1843 tomato paste samples were supplied by four different leading tomato processors in California, USA, over the tomato seasons of 2015, 2016, 2017, and 2019. The reference levels of quality traits including, natural tomato soluble solids (NTSS), pH, Bostwick consistency, titratable acidity (TA), serum viscosity, lycopene, glucose, fructose, ascorbic acid, and citric acid were determined by official methods. A portable FT-IR spectrometer with a triple-reflection diamond ATR sampling system was used to directly collect mid-infrared spectra. The calibration and external validation models were developed by using partial least square regression (PLSR). The evaluation of models was conducted on a randomly selected external validation set. A high correlation (RCV = 0.85–0.99) between the reference values and FT-IR predicted values was observed from PLSR models. The standard errors of prediction were low (SEP = 0.04–35.11), and good predictive performances (RPD = 1.8–7.3) were achieved. Proposed FT-IR technology can be ideal for routine in-plant assessment of the tomato paste quality that would provide the tomato processors with accurate results in shorter time and lower cost.

CoMFA, Molecular Docking and Molecular Dynamics Studies on Cycloguanil Analogues as Potent Antimalarial Agents

2020 ◽  
Vol 21 (1) ◽  
pp. 66
Author(s):  
Isman Kurniawan ◽  
Muhammad Salman Fareza ◽  
Ponco Iswanto
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Active Compound ◽  
Md Simulation ◽  
Partial Least Square ◽  
Least Square ◽  
Field Analysis ◽  
Data Set ◽  
Comfa Model ◽  
Antimalarial Agents

Malaria is a disease that commonly infects humans in many tropical areas. This disease becomes a serious problem because of the high resistance of Plasmodium parasite against the well-established antimalarial agents, such as Artemisinin. Hence, new potent compounds are urgently needed to resolve this resistance problem. In the present study, we investigated cycloguanil analogues as a potent antimalarial agent by utilizing several studies, i.e., comparative of molecular field analysis (CoMFA), molecular docking and molecular dynamics (MD) simulation. A CoMFA model with five partial least square regressions (PLSR) was developed to predict the pIC50 value of the compound by utilizing a data set of 42 cycloguanil analogues. From statistical analysis, we obtained the r2 values of the training and test sets that were 0.85 and 0.70, respectively, while q2 of the leave-one-out cross-validation was 0.77. The contour maps of the CoMFA model were also interpreted to analyze the structural requirement regarding electrostatic and steric factors. The most active compound (c33) and least active compound (c8) were picked for molecular docking and MD analysis. From the docking analysis, we found that the attached substituent on the backbone structure of cycloguanil gives a significant contribution to antimalarial activity. The results of the MD simulation confirm the stability of the binding pose obtained from the docking simulations.

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