ISO 16297:2020 pdf free download

05-20-2021 comment

ISO 16297:2020 pdf free download.Milk – Bacterial count – Protocol for the evaluation of alternative methods.
Any quantitative measurement in microbiology should consider that there is a requirement for the microbiological state in a sample to be regarded as one point within the coordinates of a multidimensional system, which is to be projected on to the one-dimensional scale of the method applied, i.e. plate count, flow cytometry. Aspects such as flora (types and numbers of microorganisms and their distribution), growth phase, sub-lethal damage, metabolic activity, and history, influence to a greater or lesser extent any parameter that is measured. It is evident that any projection of an n-dimensional situation onto a one-dimensional scale is bound to provide a picture of the real situation that is rather restricted. In this respect, one has to bow to the inevitable, regardless of which method of measurement is preferred.
The term anchor method’ in this document means a method internationally recognized by experts, used in legislation or by agreement between the parties. There are requirements for evaluation of an alternative method to refer to the anchor method and to be based on the examination of suitable samples for its intended use.
1 Scope
ISO 16297 specifies a protocol for the evaluation of instrumental alternative methods for total bacterial count in raw milk from animals of different species.
NOTE The document is complementary to ISO 16140-2 and ISO 8196 IDF 128 (all parts).
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content constitutes requirements of ISO 16297. For dated references, only the edition cited applies. For undated references, the latest edition of the referenced document (including any amendments) applies.
ISO 5725-1, Accuracy (trueness and precision) of measurement methods and results — Part 1: General principles and definitions
ISO 5725-2, Accuracy (trueness and precision) of measurement methods and results Part 2: Basic method for the determination of repeatability and reproducibility of a standard measurement method
ISO 8196 I IDF 128 (all parts), Milk — Definition and evaluation of the overall accuracy of alternative methods of milk analysis
ISO 16140-I, Microbiology of the food chair, — Method validation — Part 1: Vocabulary
ISO 21187 I IDF 196, Milk — Quantitative determination of bacteriological quality — Guidance for establishing and verifying a conversion relationship between results of an alternative method and anchor method results
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO 5725-1, ISO 5725-2, ISO 8196-1 I IDF 128-1, ISO 8196-2 I IDF 128-2. ISO 8196-3 I IDF 128-3 and ISO 16140-1 apply.
5.3.2 Upper limit of quantification
The upper limit of quantification is determined by the highest possible reading of the method or by methodological limitations, e.g. coincidence effects, inaccuracy in the upper measuring range, clogging of filters. Coincidence is when two or more elements of the measurand are detected simultaneously and identified as only one unit. For example, with flow cytometry, if two bacterial cells pass the detector simultaneously, they are detected as one. The coincidence effect is higher with higher concentrations of a measurand.
The upper limit of quantification is determined as the highest concentration where the instrument is still linear according to 5.Li.3.
5.3.3 Linearity ol the instrument signal
The relationship between the instrument readings and the expected values shall be linear within the concerned range of bacterial counts. Deviations from linearity may stem from non-specific signals and coincidence effects.
To evaluate linearity, use the raw data expressed in units of the alternative method without logarithmic or any other transformation.
A linearity check is at first performed visually using appropriate graphs to obtain an impression of the shape of the relationship. Whenever deviation from linearity appears evident, a quantitative parameter is calculated to indicate whether the observed trend is acceptable or not.
To achieve this, use a high bacterial count milk diluted serially with low bacterial count milk, resulting in a set of at least 10 samples covering the concentration range of interest.
Measure all samples at least four times and calculate the average result for each sample. This gives the measured value per sample. Use the measured values for the high-count milk and the low count milk to calculate values for the intermediate samples from the applied mixing ratios. This results in an expected value for each sample. Then, apply linear regression with the expected values per sample, Cr on the x-axis and the measured values per sample, C5, on they-axis.
5.4 Carry-over
Carry-over effects can occur in analytical systems that operate continuously. It derives From the transfer of a certain portion of sample material from one test sample to the next or further sample(s).
This effect can be tested by analysing consecutively milk with high bacterial count and blank samples. Carry-over causes an increase of blank sample values compared to the target range of blank sample values (value of blank sample analysed after another blank sample).
The carry-over can be expressed as percentage of the corresponding preceding milk sample. To evaluate carry-over, use the raw data expressed In units of the alternative method without logarithmic or any other transfonnation.
For evaluation of carry-over, the number of samples and the bacterial count of the milk samples should be high enough to estimate the carry-over with sufficient certainty.
The samples should be representative of the routine samples, especially regarding the storage time (longer storage time leading to higher milk viscosity and potentially higher Carry-over). One way of setting up the test is described in the example below. For detailed and theoretical aspects and alternative setups of carry-over estimation, refer to ISO 8196-3 I IDF 128-3.
As an example, one way to estimate the carry-over effect is to analyse at least 10 sets ol samples, each set containing one milk sample with very high bacterial count followed by two blank samples. Blank samples can be milk with negligible bacterial count and the high sample can be milk with a bacterial count of approximately 2 * 10 cfu/ml (where cfu/ml is colony forming units per millilitre milk sample).
Due to the design of the mechanical process of analysis, sometimes not only the next sample but also samples in a later position can be influenced by samples with high bacterial count. This can happen, for
5.6 Precision
5.6.1 General
For guidance on the determination of precision, repeatability and reproduclbiltty, see ISO 57254,
(SO 5725-2. ISO 8196-1 I IDF 128-1 and ISO 16140-1.
5.6.2 RepeatabilIty
The repeatability can be estimated from a large number (n 50 … 100) of duplicate measurements made on samples covering the whole measuring range. If the repeatability Is dependent on the level, it shall be specified as a function of the level, otherwise an average value can be used.
For total bacterial count in raw milk, the acceptability limits for the repeatability standard deviation, are:
a) units of 0,09 log units br contamination levels  2 x IO cfu/ml
b) units of 0,12 log10 units for contamination levels ‘c 2 * 10 cfu/mI.
5.6.3 ReproducibilIty
Estimate the reproducibility through an interlaboratory study in accordance with ISO 5725-2 from duplicate measurements in representative samples at the lower, medium and upper levels in the measuring range, preferably obtaiued from at least eight collaborators.
If no relationship exists between repeatability and the level, this can also be assumed to be true (or the reproducibility. LI there isa relationship between the reproducibility and the level, it shall be specified.
For total bacterial count In raw milk, the acceptability limit for the reproducibility standard deviation, 5R’ is 0,16 log10 units.
5.6.4 EvaluatIon of factors affecting the results
All non-bacteriological Factors associated with the properties of the raw milk sample that could disturb the measurements by the alternative method shall be evaluated. Examples of factors are somatic cell count, composition of milk, history of milk, sampling of milk, preservation of milk, species and breed of animals.
Carefully consider which effects dilferent factors could cause, and design experiments taking these into account.
If for example, linearity is expected to be affected by a certain factor (e.g. fat content), the linearity test should be repeated using samples with a low and high content of this affecting factor. If repeatability is expected to he affected, the repeatability test should be repeated using samples with high and low content. Certain preservatives can affect the level of the counts, To check for this, analyse a series of samples with and without the addition of a preservative.
6 Alternative method as an estimate of the anchor method
6.1 General
This clause addresses the analysis of the interrelations of the results of the alternative method and the anchor method. For the establishment and verification of a conversion relationship, see
ISO 21187 IIDF 196.
The analysis of the relation between two methods is based on the examination of test materials with both methods, covering the field of application and its spectrum of samples to be analysed with the method understudy.

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