dataesr/scientific-paragraphs-categorization huggingface dataset:
The reduction of ferric chloride was employed as an indicator of electron donor activity, which is a relevant process for the antioxidant action of polyphenols. So the antioxidant capacity of the OS extracts was evaluated with PFRAP and ICA assays. Achemlale presented a PFRAP value of 34. 42 mg EAA/g and ICA value of 8. 14 mg of EDTA/g. Results showed that the varieties having a higher DPPH and ORAC activities had similarly a greater capacity with the PFRAP and MCA tests. Different from our findings, according to Nadour  [60] , olive pulp had a ferric chloride reduction capacity of 24 mg equivalent of vitamin E per gram of product. Thus, dissemblance between the data reported in the literature and our results was justifiable. Indeed, several works have shown that antifree radical power can be influenced by the extraction procedure, the solvent (its nature and concentration), the duration, the temperature, and the variety  [61, 62].
, 2000). In the Eastern Region, fecal coliform contaminations represent 17% of the water quality problems compared to other parameters such as total solids, turbidity, biochemical oxygen demand, or dissolved oxygen. Fecal coliform bacteria in the Bang Pakong River Basin, a watershed situated in the North of the Eastern Thailand Region, exceeded surface water standard category 3, which is specified as equal to or less than 4000 bacteria per 100 mL  (Simachaya, 2003). This previous study suggested that organic loading caused by human communities, industry, pig farms, and aquaculture were the sources of pollution in this basin. In this paper, we present a predictive model of bacterial concentrations in rivers and waterways. We assessed the variability in total and fecal coliform bacteria throughout Eastern Thailand using data from a long-term monitoring program of water quality.
Sampling Sampling took place over the Eastern Thailand region in eight watersheds covering a variety of ecological situations (Fig. 1 ). In order to consider seasonal variation, sampling was conducted four times each year in February, May, August, and November which made possible to cover the range of weather regimes in Eastern Thailand: end of dry (cool) season, first peak of rainy season, start of second peak of rainy season, and start of dry season, respectively (http://www. climate. tmd. go. th/content/file/75). The data used in this paper cover a period from December 2007 until August 2016. For logistic reasons, sampling was done in December instead of November in 2007 and in 2008, and in April 2008 instead of May 2008. Additionally, several stations were added to the monitoring program in order to increase its geographical coverage.
Four stations (NK01, NK02, PT01, and PT02) were discontinued after four sampling sessions because they could not be accessed continuously to sample water. A total of 68 stations were sampled between four and 36 times resulting in a total of 1999 samples. Five stations were sampled four or six times, while the 63 others were sampled at least 19 times. The geographical coordinates (longitude, latitude) of each station were recorded. The water samples were collected in sterile 250-mL plastic bottles and transported on ice to the laboratory of the Regional Environment Office of the Chonburi Province within 24 h.
Gas-positive BGLB tubes were considered positive for the presence of TCB. Gas-positive LSB tubes were subjected to further analysis with Escherichia coli broth (ECB, Merck). The EC tubes were incubated at 44. 5 C for 24 h. Gaspositive ECB tubes were considered positive for the presence of FCB. The values of TCB and FCB (expressed in units of most probable number per 100 mL, or MPN/100 mL) were inferred using the following formula  (American Public Health Association, 1992,   ). These remote sensing data provide land cover and land use from 1992 to 2015 with a resolution of 10'' (ten arcseconds), which is equivalent to a distance of approximately 300 m. Land cover was classified into 38 categories ranging from "No data" to "Permanent snow and ice". The land cover data were extracted for Eastern Thailand (12.
TCB and FCB both had strongly skewed distributions so they were log 10 -transformed before analyses, as commonly done when analyzing such data (e. g. Lyautey et al. , 2010). Since 'Urbanization' was a continuous variable, its effect in the model was linear; however, it could be that such an effect might not be linear, for instance, urbanization may have no effect if its index is less than a threshold. Nonlinear effects of 'Urbanization' were tested by transforming the urbanization index into two categories: a threshold was chosen and a new categorical variable was defined as "urban" for the stations with 'Urbanization' greater than or equal to the threshold, or as "non-urban" if 'Urbanization' was less than the threshold. Different values of threshold were assessed. Predictions for LME models were done with best linear unbiased predictors (BLUP) which include predictions for the random and the fixed effects  (Robinson, 1991).
The predictive power of a model was calculated as the ratio of its explained variance on the total variance calculated with: R 2 ¼ 1 À b s 2 r b s 2 ; where b s 2 r is the residual variance of the model, and b s 2 is the total variance. All data analyses were done with R version 3. 5. 1  (R Core Team, 2019). The LME models were fitted and analyzed with the package lme4  (Bates et al. , 2015). The GIS data were handled with the packages rgdal  (Bivand et al. , 2018)  and raster  (Hijmans, 2018). A file with the commands used for these analyses is available as supplementary information.
Some inter-annual variation were visible for some stations (e. g. BK13, CB02, RY01, TE01). The five stations with four or six measures (BTR01, NK01, NK02, PT01, and PT02) were dropped for the subsequent analyses which were thus done with the 63 stations with at least 19 measures out of the 36 sampling occasions (see Sampling subsection). The mean TCB for each station averaged over years and seasons ranged between 479 MPN/100 mL (Station WR01 located upstream of the River Chantaburi) and 115,576 MPN/100 mL (Station BPT01 near the seamouth of the River Bang Pakong). The mean FCB ranged between 48 MPN/100 mL (Station WR01) and 83,659 MPN/100 mL (Station BPT01). Three stations had a mean TCB larger than 100,000 MPN/100 mL: PR04 (upstream of the River Phang Rat on the border of the Provinces Rayong and Chantaburi), TE02 (upstream the River Bang Pakong), and BPT01 (see Table  1 ).
26 (SE ¼ 0. 52), and the variance associated with 'Watershed' was b s 2 Watershed ¼ 0:11 (SE ¼ 0. 33), both smaller than the residual variance b s 2 ¼ 0:47 (SE ¼ 0. 68). For FCB, the final model had AIC ¼ 4516. Transforming the urbanization index as a categorical variable did not improve the fit (AIC ¼ 4527 with a threshold at 0. 5, or AIC ¼ 4535 with a threshold at 0). The estimated variances associated with the random effects were b s 2 Station ¼ 0:24 (SE ¼ 0. 49) and b s 2 Watershed ¼ 0:11 (SE ¼ 0. 33), again smaller than the residual variance b s 2 ¼ 0:47 (SE ¼ 0. 69). In order to use this final model for prediction, an artificial data set was built with all possible combinations of the five predictors ('Urbanizarion', 'Season', 'Year', 'Station', and 'Watershed'). The predicted value of the response (TCB or FCB) was calculated for each combination of the predictors.
Acknowledgements We are grateful to all the staff members who contributed to the data from the  Regional Environmental Office 13 (Chonburi) ,  Ministry of Natural Resources and Environment , including water sampling and laboratory analyses. We thank five anonymous reviewers for their constructive comments on previous versions of our paper. Many thanks to  Colin Tosh  for checking and correcting the English. Financial support was provided by a research grant from  Burapha University  through the  National Research Council of Thailand  (grant number:  March 57, 2562 ). This is publication ISEM 2021-041 SUD.
25 mm i. d. , film thickness 250 µm). The analyses were performed by injecting 0. 1 µL of sample at a split ratio of 50:1. The oven temperature was programmed starting at 40 • C, 2 • C/min up to 270 • C. For the identification of the compounds, commercial databases (Nist 98 and Wiley) and comparison of spectra with laboratory mass spectra libraries built up from pure substances and MS literature data were used  [29]. Identification of the components was also based on their GC retention indices on an apolar column, using the homologous series of n-alkanes (C 5 -C 26 ) as a reference, and their comparison with those of literature data  [30, 31]. Relative amounts of individual components were based on peak areas obtained without FID response factor correction. Three replicates were performed for each sample.
The presented data results from triplicate analysis. The MIC corresponds to the lowest concentration of essential oil that inhibits the development of a certain microorganism. The broth microdilution method was used to determine the MIC of the essential oils adapted from the Clinical Laboratory Standards Institute guidelines. The 0. 5% solution of essential oil in absolute ethanol (v/v) was freshly prepared and serially diluted in culture medium (1:1) over the concentration range of 0. 25-0. 00024% (v/v). The culture medium was used as a negative/sterility control, the culture medium inoculated with the microorganisms was used as positive/growth control, and the absolute ethanol diluted in culture medium over the concentration range of 0. 25-0. 00024% (v/v) inoculated with the microorganisms was used as a solvent control.
Free-Comment (FC) and Check-All-That-Apply (CATA) provide a contingency table containing citation counts of descriptors by products. The analyses performed on this table are most often related to the chi-square statistic. However, such practices are not well suited because they consider experimental units as being the citations (one descriptor for one product by one subject) while the evaluations (vector of citations for one product by one subject) should be considered instead. This results in incorrect expected frequencies under the null hypothesis of independence between products and descriptors and thus in an incorrect chi-square statistic. Thus, analyses related to this incorrect chi-square statistic, which include Correspondence Analysis, can lead to wrong interpretations. This paper presents a modified chi-square square framework dedicated to the analysis of multipleresponse data in which experimental units are the evaluations and which is, therefore, better suited to FC and CATA data.
This new framework includes a multiple-response dimensionality test of dependence, a multipleresponse Correspondence Analysis, and a multiple-response hypergeometric test to investigate which descriptors are significantly associated with which product. The benefits of the multiple-response chi-square framework over the usual chi-square framework are exhibited on real CATA data. An R package called "Mul-tiResponseR" is available upon request to the authors and on GitHub to perform the multiple-response chi-square analyses.
Introduction Free-Comment (FC)  (ten Kleij & Musters, 2003)  and Check-All-That-Apply (CATA)  (Adams, Williams, Lancaster, & Foley, 2007)  are word citation occurrence-based methods that aim at collecting product descriptions from consumers using either their own words or a mutual predefined list of descriptors. These descriptions are collected without any quantification or product comparison. At the panel level, the collected data constitute count data that are usually stored in a contingency table that contains the number of times each descriptor (in columns) was cited for each product (in rows). The analysis of these data starts by testing whether overall differences exist between the products. Two approaches can be distinguished to do so. The first one consists of performing a chi-square test while the second one is based on a combination of Cochran's Q statistics  (Meyners, Castura, & Carr, 2013).
These common practices assume that all citations are independent experimental units within an evaluation, which is not the case since citations of descriptors by a given subject for a given product are not independent. Instead, one evaluation, i. e. the entire set of descriptors cited by one subject for one product, should be considered as an experimental unit  (Loughin & Scherer, 1998). Indeed, considering citations as experimental units implies computing incorrect expected values under the null hypothesis of independence between products and descriptors  (Loughin & Scherer, 1998) , resulting in an incorrect chisquare statistic. Subsequent analyses of FC and CATA data based on this chi-square statistic are thus also incorrect and can sometimes lead to wrong interpretations. The present paper aims to overcome the previous limitations by introducing the multiple-response chi-square framework based on the multiple-response chi-square statistic of  Loughin and Scherer (1998).
Datasets The datasets are the same from Mahieu, Visalli, Thomas, and Schlich (2021). The study took place at the Barry Callebaut© Company, Belgium. Seventy regular consumers of milk chocolates (at least once every two weeks) were recruited among the employees of the Barry Callebaut© Company (not implied in sensory and consumer research). They performed a CATA task on four milk chocolates having different recipes: a standard Belgian milk chocolate, a Swiss milk chocolate, a milk compound chocolate, and a protein base milk chocolate. The four products were presented according to a Williams Latin square design. For each product, the CATA task was carried out according to two sensory modalities: texture in the mouth followed by flavor in the mouth. All the CATA descriptors were selected thanks to the expertise of sensory Since sensory interpretation is out of the scope of this paper dedicated to the comparison of the two chi-square frameworks, the descriptors were renamed D1, D2, etc.
Analyses All analyses were performed using R 4. 0. 2 (R Core Team, 2020). The analyses belonging to the multiple-response chi-square framework were performed using the R package "MultiResponseR" developed for this purpose by the authors. The two contingency tables were analyzed using the following procedure. An alpha risk (Type I error) of 10% was considered as the significance level. The dimensionality of the dependence between products and descriptors was determined within each chi-square framework using the dimensionality test (2000 simulations) presented in  Mahieu et al. (2020a)  for the usual chi-square framework and using the dimensionality test (2000 simulations) presented in section 2. 3. 1 for the multipleresponse chi-square framework. When at least one axis was significant, the corresponding CA (usual or multiple-response) was performed on the contingency table.
Results Table  1  shows that whatever the sensory modality and the axis considered, the eigenvalues of the CA are higher in the multipleresponse framework than in the usual one. This suggests that the usual framework underestimates the dependence between products and descriptors. This line of reasoning is reinforced by the example treated by  Loughin and Scherer (1998)  as they obtained a lower p-value (which is partly a function of the effect size) for their chi-square test in the multiple-response framework than in the usual one. On the dimensionality of the dependence, Table  1  shows that similar conclusions are provided between products and descriptors by the two chi-square frameworks concerning the flavor dataset: three axes capture significant dependence. However, the dependence on the third axis appears more certain (p = 0.
0054) in the multiple-response chi-square framework than in the usual one (p = 0. 0914). Concerning the texture dataset, only two axes capture significant dependence within the usual chisquare framework while three axes capture significant dependence within the multiple-response chi-square framework. Fig. 1  shows that for the texture dataset, the maps depicted by the two first axes of the usual CA (Fig. 1(a) ) and the MR-CA (Fig. 1(b) ) are very similar: all the products except P5 and all the descriptors have the same position on the two maps. The only difference between these maps is the location of P5 being different from P4 and closer to the origin in MR-CA (Fig. 1(b) ) as compared to usual CA (Fig. 1(a) ). The reason for this difference lies in the fact that P4 and P5 have the same profile (repartition of citations) in the usual CA.
On the contrary, the MR-CA captures that P5 received fewer citations than P4 for all the descriptors but still following the same pattern of association with the descriptors. This explains the position of P5 relative to P4: P5 deviates from independence in the same direction that P4 (same pattern of association with the descriptors) but P5 is closer to the origin of the coordinates system than P4 (received fewer citations). Concerning the third significant axis obtained with the multiple-response chi-square framework on the texture dataset (Fig. 1(c )), it mainly traduces that P5 received fewer citations than P4 for all descriptors, which is logical. Note that the usual CA is unable to capture this difference between P4 and P5, which explains the non-significance of the third axis for this CA.
For the flavor dataset, Fig. 2  shows that the spaces provided by the usual CA and the MR-CA exhibit different configurations for both products and descriptors. For every descriptor, there is at least one other product that received more citations than P3. Thus, in MR-CA, it is associated with no descriptor, which explains its position: P3 lies at the opposite of every descriptor loadings (Fig. 2(c)  & (d) ). On the contrary, in usual CA, P3 seems to be associated with D1, D5, and D6 and slightly with D2 (Fig. 2(a ) & (b)). Indeed, in usual CA, the number of citations received by P3 for every descriptor is rescaled according to its total number of received citations. Thus, the fact that for every descriptor there is at least one other product that received more citations than P3 is erased in the usual CA.
These features of P3 are the principal explanation of the differences between the spaces provided by MR-CA and usual CA, both applied on the flavor dataset. Another notable difference is between the maps depicted by the two first axes of the usual CA and of the MR-CA (Fig. 2(a)  & (c) ). On these maps, P1 and P2 appear to be more associated with D1, D4, and D6 in MR-CA as compared to the usual CA. This difference is due to the opposite phenomenon that occurred with P3: P1 and P2 received much more citations than P3 and P4 for these descriptors and the usual CA distorts this difference while the MR-CA does not. Concerning the total bootstrap tests, whatever the considered sensory modality and whatever the considered chi-square framework, the conclusions they provided were the same except when considering the pair P4/P5 and the texture dataset.
71%) lower than the one of product P3 (28. 57%) which is not significant nicely illustrates the issue of the "citation rescaling" due to considering the citations as experimental units. Since P3 and P5 were evaluated the same number of times, it is counterintuitive to have the one with the lowest proportion of citations significant and not the other. However, in the multiple-response framework, both P3 and P5 are not significantly associated with D8, which is consistent. Regarding the pair P5-D3, the association is not significant in the multiple-response chi-square framework while it is in the usual chi-square framework. This difference is due to the "citation rescaling" that occurs in the usual chi-square framework and not in the multiple-response one. Concerning the flavor dataset, several differences are shown in Fig.
Discussion To the best of our knowledge, it is the first time that a chi-square framework properly taking into account multiple-response data is introduced. The proposed analyses including the test of dimensionality, the product confidence ellipses, the pairwise product comparisons, and the product by descriptor association tests, the three of them being conducted on the significant axes, are all originals. This multipleresponse chi-square framework fits perfectly to FC and CATA data. However, this multiple-response chi-square framework is not restricted to be used only in sensory and consumer science and can be used to analyze any multiple-response data whatever the field they come from. The examples presented in this paper showed that the multipleresponse chi-square framework is better suited than the usual chisquare framework to analyze FC and CATA data.
The conclusions provided by the two chi-square frameworks are not always necessarily different. For example, they would have been almost the same on the texture dataset if P5 had not been artificially added to the dataset. The more different the citation rates (all descriptors combined) between products are, the more the conclusions drawn from the usual chi-square framework will differ from the multiple-response one. The products likely receive different numbers of citations when some products have few sensory characteristics while some others have a lot or when some products present obvious characteristics while the characteristics of the other products are more subtle; these kinds of situations are likely to occur in sensory evaluation. Since the multiple-response chi-square framework relies heavily on Monte-Carlo and bootstrap simulations, the results of the proposed analyses are not instantaneous.
Appendix:. Simulations To investigate the benefits and/or the downsides of performing the multiple-response hypergeometric tests per cell on the derived contingency table corresponding to the significant axes, simulations of sensory data were performed. For every simulation, 60 subjects, 5 products, and 10 descriptors were considered. The 5 products were considered as being evaluated by the 60 consumers, as it is common in sensory evaluation. The descriptors marginal probabilities were randomly chosen and were the following: 0. 20, 0. 56, 0. 26, 0. 23, 0. 21, 0. 30, 0. 20, 0. 42, 0. 52, 0. 75. From these marginal probabilities, the matrix of expected probabilities under the null hypothesis of independence between products and descriptors was computed. This matrix contained 50 cells (5 products × 10 descriptors).
2. 8 matrices (4 levels of dimensionality × 2 levels of deviation intensity) of probabilities were thus generated. Each of the 8 matrices contains 50 cells (5 products × 10 descriptors). For each of these 8 matrices, 1000 datasets were simulated. Each of these datasets was generated by adding 60 individual data (the subjects). Each individual data was generated by performing a random Bernoulli draw for each of the 50 cells according to the specified probability given in the matrix. For each of the 8000 datasets (8 matrices of probabilities × 1000 generated datasets), the number of significant axes was considered unknown and was determined using the dimensionality test presented in section 2. 3. 1. The multiple-response hypergeometric tests per cell were then performed on either the observed table or the derived contingency table corresponding to the significant axes returned by the test.
The p-values of the multipleresponse hypergeometric tests per cell were stored. For each combination of the factors deviation intensity (0. 1 or 0. 2), dimensionality (one axis, two axes, etc. ), and table (observed or derived) and for each of the 50 cells, the proportion of test (among the 1000 datasets) rejecting the null hypothesis was computed at the following nominal alpha risks: 5%, 7. 5%, and 10%. Then, the results from a given cell were assigned either to the group H0 if its probability was not modified or to the group H1 otherwise. Finally, the average proportion of rejection of the null hypothesis was computed within each group (H0 or H1), number of dimensions, and deviation intensity. The results are presented in Table  2. Table  2  shows that the empirical type I error never exceed the nominal alpha risk in group H0 for both approaches, which suggests that both approaches are valid.