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Test Number : NRA-FPM
Test Name : NRA ServSafe Food Protection Manager
Vendor Name : Food
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NRA-FPM test Format | NRA-FPM Course Contents | NRA-FPM Course Outline | NRA-FPM test Syllabus | NRA-FPM test Objectives

Providing Safe Food
•Foodborne Illness
•=> How Food borne Illnesses Occur
•Keeping Food Safe
Forms of Contamination
•Biological, Chemical, and Physical Contaminants
•Deliberate Contamination of Food
•Responding to a Foodborne Illness Outbreak
•Food Allergens
The Safe Food Handler
•How Food Handlers Can Contaminate Food
•A Good Personal Hygiene Program
The Flow of Food: An Introduction
•Hazards in the Flow of Food
•Monitoring Time and Temperature
The Flow of Food: Purchasing, Receiving, and Storage
•General Purchasing and Receiving Principles
The Flow of Food: Preparation
•Cooking Food
•Cooling and Reheating Food
The Flow of Food: Service
•Holding Food
•Serving Food
Food Safety Management Systems
•Food Safety Management Systems
Safe Facilities and Pest Management
•Interior requirements for a Safe Operation
•Emergencies that Affect the Facility
•Pest Management
Cleaning and Sanitizing
•Cleaning and Sanitizing
•Cleaning and Sanitizing in the Organization
•Organisms That Cause Foodborne Illness
This Syllabus is strictly adhered to and there are no provisions for ‘practical experience.
I. Providing Safe Food including the ethical responsibility of operators to provide a safe product, how to produce such a product, what can happen when sanitation is breached, common sense approach to maintaining good sanitation, and the role of the NYS Board of Health in keeping the food supply safe, the FAT TOM concept.
II. Forms of Contamination including biological, chemical, and physical contaminants, how to prevent common problems and outbreaks of foodborne illnesses, specific pathogens and their preferred growing environments, the four phases of bacterial growth.
III. The Safe Foodhandler including effective training and protocols for staff to maintain good sanitation, the role of personal hygiene in maintain safe food handling practices, making every day a "health inspection day".
IV. The Flow of Food V. Food Prep and Service including the role of HACCP in the food safety arsenal, following potentially hazardous foods through the operation from purchasing to post production reports.
VI. Food Safety Management
VII. The Safe Facility including modeling safe food handling practices, providing tools and training for safe food handling, response to outbreaks of foodborne illness (perceived or real), discussion keeping the facility pest and contaminant free including proactive tactics, how to read and use Material Safety Data Sheets. VIII. Cleaning and sanitizing and National Certification test including the definitions of the terms clean versus sanitary, different types of sanitizers including chlorides, and quaternary sanitizing agents and their uses, sanitizing practices for major equipment, surfaces, service ware and hand tools.

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Food learning

A desktop researching workflow for uncooked food spectroscopic classification in a future trade | NRA-FPM test Questions and PDF Download


First and prior to supervised dimensionality discount by way of Partial Least Squares (PLS) regression, regular general Variate (SNV) normalization scheme17 and exceptionally under its potent version, RNV18 changed into employed to normalize the bought spectra S, in line with:

$$ s_i^snv = \fracs_i - median\left( S \correct)mad\left( S \correct) $$


where si is the ith spectrum and sisnv the ith normalized spectrum. MAD stands for Median Absolute Deviation (mad)33; a strong variability metric of a univariate pattern of quantitative information s1,s2,…,sn. MAD is computed as:

$$ mad = median\left( s_i \appropriate) - median\left( S \right) $$


The above normalization scheme is used for records high-quality enhancement, discount of the correlated tips along the wavelengths of the spectra and eliminate the multiplicative noise originating from the acquisition method inherent with the intention to enhance the downstream evaluation. The equal scheme for statistics normalization has been utilized in a different work by means of their laboratory34.

Then the normalized information/spectra, are passed to the subsequent step of processing, i.e. dimensionality reduction, and more peculiarly PLS-based mostly supervised dimensionality discount. In conventional, PLS regression19 is used for finding relationships between two facts matrices, X and Y by the use of a linear multivariate model. it's a well-liked formula with wide purposes in numerous scientific fields and it’s wonderful for functions the place the variety of variables (wavelength/wavenumbers in their case) exceeds the number of samples. PLS resembles in many methods with foremost element Analysis35 (PCA) because it transforms and maps a group of possibly correlated observations to a group of linearly uncorrelated values referred to as accessories and the house these add-ons define. Some of these accessories can then be used as regression coefficients for the records to confederate dimensional discount. PLS in contrast to PCA takes into consideration the latent structure of no longer best the impartial variables (predictors) however that of the elegant (responses) as smartly. this is executed by using a training set to find the multi-dimensional variance route from the predictors’ area, the place the highest multidimensional variance path is defined into the space of the responses. this way, you'll be able to predict the response of latest records based best on the predictors the use of a mannequin created in a supervised manner.

The number of the components to “grasp” in a PLS regression19 modeling utility is crucial considering that wrong choice can lead or keep away from “over-fitting”, i.e. a mannequin with excessive accuracy on the working towards dataset however with little to zero predictive energy on new, impartial samples. to conquer this situation constantly some kind of model validation method is used with the intention to verify how the resulting mannequin will generalize with new untrained records, with essentially the most conventional being the move-validation. throughout go-validation the practicing facts is cut up into practicing and validation units in line with a user-described ratio. Then, recursively, a number of fashions are created and expert the use of the working towards set, every new one the usage of a further component than the final. each and every model is then evaluated with the validation set in response to the suggest square error (MSE) of the anticipated versus the exact values (Fig. 3a). Intuitively, the smaller the MSE is, the more suitable the mannequin will perform against the prediction of the response variable with new observations. Prediction error estimation with “unseen” statistics; i.e. with statistics that have not been used to educate the model helps avoiding overfitting and additionally provide consequences below a good assessment of distinctive regressions exhibiting a variability on the variety of covariates36. this manner the inclusion of the minimal number of add-ons in their mannequin that most fulfilling describe the statistics is reached. Herein, tenfold go-validation changed into performed and the highest number of components has been set to one hundred. The MSE metric for the calibration gets greater and more advantageous the greater components are delivered to the mannequin (information not proven). nevertheless, the more complicated the mannequin, the greater biased towards overfitting it gets. as a consequence, the go-validation values of the MSE are essential, with a view to opt for the less complicated model with the optimum efficiency. determine 3a items precisely these values, exhibiting a minimum in the MSE, which happens for notebook = forty one. adding greater fundamental add-ons, the go validation MSE gets really worse, meaning that the model begins to overfit and loses its generalization.

determine three

Supervised PLS dimensionality discount overview: (a) imply square error vs. variety of add-ons (minimal MSE @ forty one accessories) across tenfold cross-validation, (b) pattern spectra for each and every type class, (c) weights from PLS for every coefficient, i.e. wavelength.

subsequent within the classification pipeline, is the help Vector computer (SVM) based mostly modeling with using the facts transformed into the PLS house and on the selected variety of components, i.e. problem dimensionality. aid Vector Machines (SVMs) are models, created in a supervised manner, used for classification and regression analysis20. SVMs is a smartly-ideal classification technique when the working towards facts incorporates large variety of variables in relationship to the variety of observations. In SVM each pattern x that incorporates n variables is handled as an n-dimensional vector. The goal is to discover a surface, a hyperplane that is capable of separate the vectors in line with their corresponding classification. There can also be many hyperplanes that can classify the data. From those hyperplanes those that have the bottom generalization error, i.e. most excellent predictive means of accurate untrained records, deserve to be chosen. One average approach to achieve this is by means of choosing the hyperplane that represents the greatest “separation”, or margin, between the classes. This hyperplane has the biggest distance from the closest practicing data factor of any class. in lots of situations the units to discriminate cannot be linearly separable. to beat this, the sets can be mapped from a finite to a higher dimensional area, in an attempt to make the separation easier37. here's referred to as the “kernel trick”, used for projecting and classifying the records from a stronger dimensional space without ever computing the corresponding vectors in that house but reasonably by computing the inner items of each pair of vectors in the modified house.

In certain, given a practicing set of information \(\left( x_i ,y_i \correct), i = 1, \ldots , l\) with \(x_i \in R^n\) and \(y \in \left[ - 1, 1 \right]^l\), SVM finds the solution to right here optimization issue:

$$ min_w,b,\xi \frac12w^T w + C\mathop \sum \limits_i = 1^l \xi_i $$

$$ \textdiscipline\,\textual contentto:\,y_i \left( w^T \varphi \left( x_i \correct) + b \correct) \ge 1 - \xi_i , \xi_i \ge 0 $$


The characteristic φ maps the vectors xi to the bigger dimensional space, C is the penalty parameter of the error term and \(okay\left( x_i ,x_j \correct) \equiv \varphi \left( x_i \appropriate)^T \varphi \left( x_j \appropriate)\) is the kernel function. there are lots of kernel features, where the three most regular are:

$$ \textual contentlinear:\,k\left( x_i ,x_j \appropriate) = x_i^T x_i $$


$$ \textual contentpolynomial:\,ok\left( x_i ,x_j \right) = (\gamma x_i^T x_i + r)^d , \gamma > 0 $$


$$ \textual contentradial\,\textfoundation\,\textual contentfeature\,( \textual contentRBF):\,k\left( x_i ,x_j \correct) = e^ - \gamma x_i - x_j^2 $$


SVM multiclass classification is carried out through essentially the most commonly, in multiclass complications, used One-vs-the-relaxation (OvR) approach, i.e. one classifier is geared up per category. As brought up within the scikit-learn38 webpage for the multiclass algorithms, for each and every classifier, the class is geared up in opposition t all the other courses. The closing output is the classification that corresponds to the SVM with the biggest margin. OvR method is computational productive, on the grounds that best n_classes classifiers are vital, and also it's interpretable: each classification is represented by means of only 1 classifier, accordingly it is possible to have access to a class by way of inspecting its corresponding classifier. In their case matrix X includes 1815 (practising samples) normalized FTIR training samples with 1,seven hundred variables/wavelengths each and every, whereas Y matrix is a single column matrix together with the type (coded as a number from 0 to 6) for each corresponding pattern type. First, tenfold go-validation became used to be able to check the minimal number of PLS components that reduce the suggest squared error (please seek advice from Fig. 3a). The mean square error is computed via the anticipated values of the classification (i.e. class quantity) with admire to the genuine type number as described by using the matrix Y. within the tenfold, the practising set is shuffled after which split it into ten subsets (folds). From these subsets, a single set is kept out of the working towards for validation and the mannequin is informed the usage of the leisure 9 sets and repeated 10 instances with a different validation set each time. This manner has been employed so as to consider a hundred diverse, when it comes to variety of components, PLS models (ranging from 1 to a hundred add-ons) (please refer to Fig. 3a). The pass-validation effects for their facts resulted in 41 add-ons because the choicest number for use. Thereafter, the model became informed using the entire of the practicing statistics, deliberating only the premiere number of add-ons. At this point, it should be mentioned that this technique reduced the dimensionality from 1,700 to just forty one elements. Afterwards, the coach statistics are changed using the PLS mannequin to get the decreased practising set, which is then used because the working towards set for the help vector computing device. with a view to tune the parameters, i.e. locate the most desirable hyper-parameter values of SVM, grid search approach39 became employed. Grid search is an exhaustive search via a manually specific subset of the parameter space, mixed with pass validation, in an try and compute the superior kernel and parameters for the SVM classification. Herein the kernels proven have been: linear, radial groundwork characteristic (rbf) and polynomial, the search range for C parameter is determined as [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000], while for the γ parameter [10−6, 10−1] in logarithmic scale and degree = 2, three, four and 5. The outcomes for the hyper-parameters grid search resulted in opting for the linear kernel and C = a hundred, because the superior classifier parameters for their records. Afterwards the SVM is trained using those most beneficial kernel and parameters. eventually, the equal method is applied to the check set (described previously) after applying on them the robust SNV normalization resulted from the practicing set and the corresponding transformation to the same PLS house as the practising set.

The SVM classification mannequin has been evaluated on the test records, in terms of accuracy, F1-score, sensitivity, precision, specificity, Matthews’s correlation coefficient (MCC), Informedness, Markedness, in complete and per class (records shown in table SI1). furthermore, the probabilities of the SVM classifier for each and every pattern (test set) were approximated in response to Platt’s scaling approach, in an effort to explain any misclassifications and attempting to interpret the consequences.

substances and samples

The meals types under consideration come from 7 distinctive courses, namely beef40,41, pork42,43, chicken44, farmed whole ungutted gilhead sea bream45, competent-to-consume rocket34, child-spinach34 and pineapple. The samples were all subjects from experiments and corresponding experimental setups of spoilage research, that have been previously published34,40,forty one,42,forty three,44,45 and the reader can confer with table 2 and the corresponding publications for more details. In ordinary, each and every food class consisted of as a minimum 2 impartial batches of samples and a pair of replicates of each sample. From all these samples the corresponding spectral facts had been got with FTIR spectroscopy, as described next. greater mainly, within the case of pork, a number of samples got here from Tsakanikas et al.forty two and other extra samples got here from a spoilage connected experimental setup Fengou et al.forty three. in particular, minced pork samples were organized and packaged in meals trays, placed in one styrofoam tray (duplicate samples) and wrapped with air-permeable polyethylene plastic46 hang film. Samples have been kept at distinctive isothermal situations (4, eight, and 12 °C) and below dynamic circumstances (periodic temperature alternate from 4 to 12 °C) in high precision (± 0.5 °C) programmable incubators (MIR-153, Sanyo electric powered Co., Osaka, Japan) for a highest time length of 14 days. moreover, two chronically independent batches have been used with a view to boost samples’ variability. in regards to the pineapple samples, they have been saved in their customary packages at three isothermal temperatures, i.e. 4, eight and 12 °C, and at a dynamic profile (8 h at 4 °C, 8 h at 8 °C and eight h at 12 °C). Sampling become performed at usual time intervals counting on the storage temperature for a maximum length of 10 days. In complete, 4 unbiased experimental replicates had been considered leading to 318 samples of pineapple. In abstract, the whole samples used in this look at have been 1815 practicing samples (four hundred red meat, 380 pork, 300 fish, 280 pineapple, one hundred ninety rocket, 85 hen, a hundred and eighty spinach), and 240 test samples (44 pork, 52 pork, 26 fish, 38 pineapple, 29 rocket, 15 hen, 36 spinach).

From the aforementioned description of the records used herein, it is obvious that by way of the excessive range of samples’ foundation (distinct batches and in some situations even diverse time durations and individuals conducting the experiments) and state (sampling situation over a spoilage experimental setup—resulting in varying biochemical houses of the samples and for that reason range in their corresponding FTIR spectra), it changed into feasible to import this tips into the predictive models to simulate real lifestyles conditions, seeing that the datasets have been obtained below different situations of temperature, packaging, storage time and diploma of microbiological illness, apart from distinctive batches. this fashion, it will also be ensured that whatever the classification influence, the mannequin could be satisfactory effective and general to the enter, considering that for distinct circumstances the samples (in the same trial class) are degraded in another way as well as their chemical profile. So, it's obvious that the assessment scheme adopted herein and more importantly, the facts where the classification fashions were educated, are unbiased (even inside the identical pattern class) with massive variability, ensuing in the development of a classifier that is robust, regular and consequently respectable.

records acquisition—FTIR spectroscopy

The FTIR spectral data were accrued using a ZnSe forty five° HATR (Horizontal Attenuated complete Reflectance) crystal (PIKE technologies, Madison, Wisconsin, usa), and an FTIR-6200 JASCO spectrometer (Jasco Corp., Tokyo, Japan). The spectra acquisition technique carries chopping a small element from every trial and positioned to the crystal plate, covered with a small piece of aluminum foil. The particular crystal works at a refractive index of 2.4 and a depth of penetration of two.0 μm @ 1,000 cm−1. Then the received spectra had been processed and gathered by the Spectra manager™ Code of Federal laws (CFR) application version 2 (Jasco Corp.). The corresponding wavenumber latitude is four,000–four hundred cm−1, while a hundred scans with a decision of four cm-1 and a complete integration time of two min have been accumulated. The FTIR spectra that have been utilized in additional analyses were in the approximate wavenumber range of 2,seven-hundred–1,000 cm−1, i.e. 1,seven-hundred wavelengths (sample points), resulted by eliminating the water peak beginning at ~ 2,seven-hundred cm−1 and ignoring the latitude [400–1,000 cm−1] as it exceptionally represents noise.

Implementation and performance

The whole pipeline has been applied in Python 2.7 employing scikit-learn library39. The code is OS impartial and require the libraries indicated within the supply code and at the import circumstances.

utility and data availability

The python scripts together with the records used for practicing and look at various of the equipment can be found at zenodo,

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Zend [6 Certification Exam(s) ]

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