Pathogenic Escherichia coli and Salmonella Can Survive in Kimchi during Fermentation

ABSTRACT The survival of Escherichia coli and Salmonella strains during diced white radish kimchi fermentation was studied. Kimchi batches inoculated with the pathogens were fermented at 4, 15, and 25°C for 42 to 384 h. Cell counts of E. coli and Salmonella were enumerated on E. coli–coliform count plates and xylose lysine deoxycholate agar, respectively. Baranyi (primary model) and polynomial (secondary model) models, validated by root mean square error, were used to describe the kinetic behavior of the pathogens. In the primary model, both the death phase shoulder (E. coli: 208.18 to 8.25 h, 4 to 25°C; Salmonella: 79.91 to 0.97 h, 4 to 25°C) and bacterial cell counts (log CFU per gram per hour) decreased with increasing temperature (P < 0.05) (death rate: E. coli: −0.02 to −0.09, 4 to 25°C; Salmonella: −0.01 to −0.10, 4 to 25°C), the results being equally significant in the secondary model. The root mean square error (0.480 to 0.485) showed that the model performance was good. The fermentation temperature and time are the critical factors that control pathogenic E. coli and Salmonella in kimchi.

Download Full-text

Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature

Geoscientific Model Development ◽

10.5194/gmd-7-1247-2014 ◽

2014 ◽

Vol 7 (3) ◽

pp. 1247-1250 ◽

Cited By ~ 1200

Author(s):

T. Chai ◽

R. R. Draxler

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Model Evaluation ◽

Mean Absolute Error ◽

Evaluation Studies ◽

Model Performance ◽

Absolute Error ◽

Average Error ◽

Mean Square

Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric, whereas Willmott et al. (2009) indicated that the sums-of-squares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance.

Download Full-text

Characterization of Synthetic Non-Metallic Inclusions Consisting of TiN, Ti2O3, and Oxides of Al2O3 and MgO·Al2O3 Spinel Using Raman Spectroscopy

Metals ◽

10.3390/met11101549 ◽

2021 ◽

Vol 11 (10) ◽

pp. 1549

Author(s):

Francis Gyakwaa ◽

Tuomas Alatarvas ◽

Qifeng Shu ◽

Matti Aula ◽

Timo Fabritius

Keyword(s):

Raman Spectroscopy ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Raman Spectra ◽

Model Performance ◽

Qualitative Evaluation ◽

Quantitative Prediction ◽

Mean Square ◽

Improved Model

Steel quality and properties can be affected by the formation of complex inclusions, including Ti-based inclusions such as TiN and Ti2O3 and oxides like Al2O3 and MgO·Al2O3 (MA). This study assessed the prospective use of Raman spectroscopy to characterize synthetic binary inclusion samples of TiN–Al2O3, TiN–MA, Ti2O3–MA, and Ti2O3–Al2O3 with varying phase fractions. The relative intensities of the Raman peaks were used for qualitative evaluation and linear regression calibration models were used for the quantitative prediction of individual phases. The model performance was evaluated with root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP). For the raw Raman spectra data, R2 values were between 0.48–0.98, the RMSECV values varied between 3.26–14.60 wt%, and the RMSEP ranged between 2.98–15.01 wt% for estimating the phases. The SNV Raman spectra data had estimated R2 values within 0.94–0.99 and RMSECV and RMSEP values ranged between 2.50–3.26 wt% and 2.80–9.01 wt%, respectively, showing improved model performance. The study shows that the specific phases of TiN, Al2O3, MA, and Ti2O3 in synthetic inclusion mixtures of TiN–(Al2O3 or MA) and Ti2O3–(Al2O3 or MA) could be characterized by the Raman spectroscopy.

Download Full-text

Estimation of coarse dead wood stocks in intact and degraded forests in the Brazilian Amazon using airborne lidar

Biogeosciences ◽

10.5194/bg-16-3457-2019 ◽

2019 ◽

Vol 16 (17) ◽

pp. 3457-3474 ◽

Cited By ~ 1

Author(s):

Marcos A. S. Scaranello ◽

Michael Keller ◽

Marcos Longo ◽

Maiza N. dos-Santos ◽

Veronika Leitold ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Dead Wood ◽

Model Performance ◽

Forest Carbon ◽

Airborne Lidar ◽

Mean Square ◽

Improved Model ◽

Relative Root

Abstract. Coarse dead wood is an important component of forest carbon stocks, but it is rarely measured in Amazon forests and is typically excluded from regional forest carbon budgets. Our study is based on line intercept sampling for fallen coarse dead wood conducted along 103 transects with a total length of 48 km matched with forest inventory plots where standing coarse dead wood was measured in the footprints of larger areas of airborne lidar acquisitions. We developed models to relate lidar metrics and Landsat time series variables to coarse dead wood stocks for intact, logged, burned, or logged and burned forests. Canopy characteristics such as gap area produced significant individual relations for logged forests. For total fallen plus standing coarse dead wood (hereafter defined as total coarse dead wood), the relative root mean square error for models with only lidar metrics ranged from 33 % in logged forest to up to 36 % in burned forests. The addition of historical information improved model performance slightly for intact forests (31 % against 35 % relative root mean square error), not justifying the use of a number of disturbance events from historical satellite images (Landsat) with airborne lidar data. Lidar-derived estimates of total coarse dead wood compared favorably with independent ground-based sampling for areas up to several hundred hectares. The relations found between total coarse dead wood and variables quantifying forest structure derived from airborne lidar highlight the opportunity to quantify this important but rarely measured component of forest carbon over large areas in tropical forests.

Download Full-text

Quantifying the Robustness of a Broth-Based Escherichia coli O157:H7 Growth Model in Ground Beef†

Journal of Food Protection ◽

10.4315/0362-028x-68.11.2301 ◽

2005 ◽

Vol 68 (11) ◽

pp. 2301-2309 ◽

Cited By ~ 9

Author(s):

DANILO T. CAMPOS ◽

BRADLEY P. MARKS ◽

MARK R. POWELL ◽

MARK L. TAMPLIN

Keyword(s):

Escherichia Coli ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Growth Model ◽

Standard Error ◽

Ground Beef ◽

Escherichia Coli O157 ◽

Mean Square ◽

Data Set

The robustness of a microbial growth model must be assessed before the model can be applied to new food matrices; therefore, a methodology for quantifying robustness was developed. A robustness index (RI) was computed as the ratio of the standard error of prediction to the standard error of calibration for a given model, where the standard error of calibration was defined as the root mean square error of the growth model against the data (log CFU per gram versus time) used to parameterize the model and the standard error of prediction was defined as the root mean square error of the model against an independent data set. This technique was used to evaluate the robustness of a broth-based model for aerobic growth of Escherichia coli O157:H7 (in the U.S Department of Agriculture Agricultural Research Service Pathogen Modeling Program) in predicting growth in ground beef under different conditions. Comparison against previously published data (132 data sets with 1,178 total data points) from experiments in ground beef at various experimental conditions (4.8 to 45°C and pH 5.5 to 5.9) yielded RI values ranging from 0.11 to 2.99. The estimated overall RI was 1.13. At temperatures between 15 and 40°C, the RI was close to and smaller than 1, indicating that the growth model is relatively robust in that temperature range. However, the RI also was related (P < 0.05) to temperature. By quantifying the predictive accuracy relative to the expected accuracy, the RI could be a useful tool for comparing various models under different conditions.

Download Full-text

Root mean square error (RMSE) or mean absolute error (MAE)?

Geoscientific Model Development Discussions ◽

10.5194/gmdd-7-1525-2014 ◽

2014 ◽

Vol 7 (1) ◽

pp. 1525-1534 ◽

Cited By ~ 93

Author(s):

T. Chai ◽

R. R. Draxler

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Model Evaluation ◽

Mean Absolute Error ◽

Evaluation Studies ◽

Model Performance ◽

Absolute Error ◽

Average Error ◽

Mean Square

Download Full-text

Estimation of Coarse Woody Debris Stocks in Intact and Degraded Forests in the Brazilian Amazon Using Airborne Lidar

10.5194/bg-2019-75 ◽

2019 ◽

Author(s):

Marcos A. S. Scaranello ◽

Michael Keller ◽

Marcos Longo ◽

Maiza N. dos-Santos ◽

Veronika Leitold ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Dead Wood ◽

Woody Debris ◽

Model Performance ◽

Forest Carbon ◽

Airborne Lidar ◽

Mean Square ◽

Relative Root

Abstract. Coarse dead wood is an important component of forest carbon stocks, but it is rarely measured in Amazon forests and is typically excluded from regional forest carbon budgets. Our study is based on line intercept sampling for fallen coarse dead wood conducted along 103 transects with a total length of 48 km matched with forest inventory plots where standing coarse dead wood was measured in the footprints of larger areas of airborne lidar acquisitions. We developed models to relate lidar metrics and Landsat time series variables to coarse dead wood stocks for intact, logged, and burned or logged and burned forests. Canopy characteristics such as gap area produced significant individual relations for logged forests. For total fallen plus standing coarse dead wood (hereafter defined as total coarse dead wood), the relative root mean square error for models with only lidar metrics ranged from 33 % in logged forest to up to 36 % in burned forests. The addition of historical information improved model performance slightly for intact forests (31 % against 35 % relative root mean square error), not justifying the use of number of disturbances events from historical satellite images (Landsat) with airborne lidar data. Lidar-derived estimates of total coarse dead wood compared favorably to independent ground-based sampling for areas up to several hundred hectares. The relations found between total coarse dead wood and structural variables derived from airborne lidar highlight the opportunity to quantify this important, but rarely measured component of forest carbon over large areas in tropical forests.

Download Full-text

Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance

Climate Research ◽

10.3354/cr030079 ◽

2005 ◽

Vol 30 ◽

pp. 79-82 ◽

Cited By ~ 1408

Author(s):

CJ Willmott ◽

K Matsuura

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Mean Absolute Error ◽

Model Performance ◽

Absolute Error ◽

Mean Square ◽

Average Model ◽

The Mean

Download Full-text

Simulation of phenology, total nutrient uptake and grain yield of wheat under different irrigation and nitrogen application managements in Hisar, India using the DSSAT-CSM-CERES-Wheat model

Indian Journal of Agricultural Research ◽

10.18805/ijare.a-4722 ◽

2018 ◽

Author(s):

Mukesh Kumar ◽

R.K. Pannu ◽

Bhagat Singh

Keyword(s):

Root Mean Square Error ◽

Grain Yield ◽

Nutrient Uptake ◽

Mean Square Error ◽

Root Mean Square ◽

Model Performance ◽

Coefficient Of Determination ◽

Average Error ◽

Mean Square ◽

Nitrogen Levels

The purpose of this study was the calibration and validation of DSSAT-CSM-CERES-Wheat model (v4.5) for wheat in Hisar conditions. The DSSAT-CSM-CERES-Wheat model was calibrated with the field experimental data of rabi 2010-11 having 3 levels of irrigation (I1-one irrigation at crown root initiation [CRI], I2- two irrigations at CRI and heading and I3- four irrigations at CRI, late tillering, heading and milking) and 5 nitrogen levels (0, 50, 100, 150 and 200 kg N/ha) and validated with data of experiment rabi 2011-12 conducted at Hisar (29°10’ N and 75°46’ E). The model performance was evaluated using average error (Bias), root mean square error (RMSE), normalized root mean square error (nRMSE), index of agreement (d-stat) and coefficient of determination (r2), and it was observed that DSSAT-CSM-CERES-Wheat model was able to predict the phenology, total nutrient uptake and grain yield of wheat with reasonably good accuracy. The simulated results were within the permissible limit of the error (error % less than ±15).

Download Full-text

Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

Remote Sensing ◽

10.3390/rs13091630 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1630

Author(s):

Yaohui Zhu ◽

Guijun Yang ◽

Hao Yang ◽

Fa Zhao ◽

Shaoyu Han ◽

...

Keyword(s):

Remote Sensing ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Minimum Temperature ◽

Price Regulation ◽

Flowering Period ◽

Mean Square ◽

Prevention Measures ◽

Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

Download Full-text

Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Forests ◽

10.3390/f12081020 ◽

2021 ◽

Vol 12 (8) ◽

pp. 1020

Author(s):

Yanqi Dong ◽

Guangpeng Fan ◽

Zhiwu Zhou ◽

Jincheng Liu ◽

Yongguo Wang ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Low Cost ◽

Reference Value ◽

Tree Height ◽

Point Clouds ◽

Tree Structure ◽

Mean Square ◽

Tree Model

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

Download Full-text