FOOD ANALYSIS TRANSES.pdf

FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 Ensuring no harmful substances present i...

FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 Ensuring no harmful substances present in any food products L1: Introduction to Food Analysis Ensuring that harmful substances are effectively eliminated before the product is consumed A FOOD ANALYSIS A3.2 GOVERNMENT REGULATIONS Discipline dealing with the development, application and study of analytical Maintain food supply quality procedures for characterizing the Ensure wholesome and safe food from properties of foods and their the industry constituents. (McClements (2003) FDA are conducting random Food analysis is the application of food checking or testing of various food chemistry. Understanding the food products so that they'll be able to concept. How do they behave? What are verify whether the declared we going to be? The importance of ingredients and nutritional content moisture, protein, dietary fiber to food and are within the limit or not. how to mitigate your microbial proliferation by using the profile of the food product that you're going to analyze. Inform consumers of nutritional composition Enable fair competition among food A2 USES OF FOOD ANALYSIS companies Eliminate economic fraud Economic fraud - company na Provide information about food nandadaya. e.g nakalagay is 95% characteristics sugar tas indi pala, adulterated pala sya ng starch kase mas mura Can be physical, chemical, or than sugar microbial properties Understanding factors that determine food properties Philippine To economically produce consistent safe, Food nutritious and desirable foods Regulatory Agencies For consumer to make informed choices (RA10611) Assuring food authenticity Food Safety Regulation A3 REASONS FOR ANALYZING FOOD Coordinating Board Food Safety Government Regulations Quality Control Research and Development Food Safety regulation coordinating boards A3.1 FOOD SAFETY PAGE 1 NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 the arm or the Ingredient Suppliers implementers of the Analytical Service Laboratories Food Safety Act of the Government Laboratories Philippines of 2013 University Research Laboratories As per the RA11052 (Philippine Food Technology FDA is under the Act 2018): mandate or Mandata Section 4: The Scope of the Practice of Food Department of Health, Technology together with the Bureau (C) Evaluation of the microbiological, physical, of Quarantine. chemical, sensory and functional properties of food. Bureau of Quarantine - (D) Certification of the analysis of the the food can also be a microbiological, physical, chemical, sensory and source of pandemic or functional properties of food viruses. A viruses might harm the food chain in the Philippines A3.2.2 ROLE OF FOOD ANALYSIS ON: GOVERNMENT REGULATIONS AND RECOMMENDATIONS DOH Food Standards (Codex Alimentarius Safety Commission, BAFS, FDA, BPS) Regulatory ❖ Mandatory Standards Agencies ➔ Standards of Identity - anong uri o klase ng pagkain? DA Food ➔ Standards of Quality - Safety dictates the price Regulatory ➔ Standards of Fill-off-container - gaano Agencies kalaki ang headspace ❖ Voluntary Standards ➔ Standards of Grade under the mandate of Nutrition Labeling, Health Claims department of Authenticity Food Inspection and Grading agriculture e.g BAI - Bureau of Animal Industry A3.2.3 CODEX ALIMENTARIUS, BAFS, NMIS mark indicates that FDA the meat are not butcha A. Codex Alimentarius A3.2.1 WHO ANALYZES FOOD? Food Manufacturers PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 2  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 internationally harmonized standards for primary and post-harvest agriculture and fishery products Food and Drug Administration – Department of Health (FDA-DOH): develops standard for processed foods A3.2.4 NUTRITIONAL LABELING, HEALTH CLAIMS A. Nutritional Labeling International reference pero tailored fit base in country Nutritional Labeling - Tolerance Limit e.g Aluminum containing baked products ay ban sa EU and Australia but in Philippines hindi, kaya hindi pwede sa trade policy (or export since ban nga) Develop standards for food groups and B. Health Claims specific commodities For consumer protection and facilitation of international trade BAFS (Bureu of Agriculture and Fishery Standards) develops PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 3  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 A3.3 ROLES OF FOOD ANALYSIS ON: QUALITY CONTROL Characterization o f Raw Materials Monitoring o f Food Properties during processing Characterization of Final Product Gluten free - similar to allergen, even small amount or not direct ingredient can still cause allergies ➔ e.g milk kase may whey protein na nagcacause ng casein or allergic reaction Identifying the source of the problems with unacceptable products Hazard Analysis Critical Control Point A3.4 RESEARCH AND DEVELOPMENT (HACCP ) Basic research and product development Retain sample - reference or Characterize the properties of foods identification in addressing Ascertain the role that each ingredient complaint plays in determining the overall properties of foods Determine how processing conditions affect food properties A4 PROPERTIES OF FOOD ANALYZED Composition Structure Physicochemical Properties Sensory Attributes PROPERTIES OF FOOD ANALYZED A. Composition Determines safety, nutritional quality, physicochemical PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 4  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 properties, quality attributes ❖ It cannot be used to and sensory characteristics provide information about ➔ Chemical Constituents the safety, composition or nutritional value of food ➔ Specific atoms ➔ Specific molecules Sensory attributes are ➔ Types o f molecules correlated quantities that can ➔ Specific substances be measured using objective analytical techniques e.g B. Structure texture analyzer Food structure at different levels: ➔ Molecular Structure (1-100 nm) ➔ Types of molecules, 3-D structures, interactions ➔ Microscopic structure (10 nm-10 0𝜇𝑚) ➔ Emulsion droplet, fat crystals, small air cells A5 STEPS IN ANALYZING FOODS ➔ Macroscopic structure (>10 0𝜇𝑚) ➔ Sugar granules, large air 1. Selection of Analytical Method cells, chocolate chips 2. Sampling and Sample Preparation 3. Performance of Analytical Procedure size of sugar, or the 4. Statistical Analysis of Measurement granules of sugar is 5. Data Reporting directly proportional to its solubility. CLASSIFICATIONS OF ANALYTICAL METHODS C. Physicochemical Properties Optical properties A. Classical B. Instrumental Rheological properties Methods Methods Stability Flavor Separation: Separation: Precipitation, HPLC, GC, SFC, D. Sensory Attributes extraction, electrophoresis Often the ultimate test for the distillation Qualitative/ acceptance or rejection of a Qualitative: Quantitative: particular foods spot tests, Light Disadvantages boiling point, absorption/ ❖ Time consuming and melting point , Emission expensive solubility, odor, fluorescence, ❖ Tests are not objective refractive index electrode ❖ It cannot be used on Quantitative: potential , materials that contain Gravimetric, mass-to-charge poison or toxins titrimetric ratio, conductivity etc. PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 5  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 A6 CHOOSING AN ANALYTICAL TECHNIQUE Books Tabulated Official Methods of Analysis e.g AOAC and AACC Journals Equipment and Reagent suppliers Internet Developing a new technique METHODS SELECTION IN FOOD ANALYSIS B. MATRIX EFFECT Performance of many analytical methods is affected by the food matrix (major chemical components) Digestion and extraction procedures necessary for accurate analytical A. CHARACTERISTICS OF THE METHOD results are highly dependent on food Specificity matrix. Safety The triangle scheme created nine Destructive - sample cannot categories of food matrices according be consider a good product to high, medium, and low levels of fat kase ididisposed after or carbohydrates and proteins non-destructive - hindi Analytical methods ideally would be dinidisposed geared to handle each of the nine On-line /O ff-line combinations Official Approval Food triangle categorization enables Nature of Food Matrix analyst to choose the appropriate SRM for method development, for optimization, or for confidence of routine testing. Food Triangle Categorization rely on food composition table e.g FNRI PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 6  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 Routine Testing - madalas na ginagawang testing before and after production AOAC believes that one or two foods within a sector are representative of other foods within that sector, and if an analytical method provides accurate results for the test foods, it should also provide accurate results for the other foods of the same sector A7 SUMMARY Food scientists from different sectors including government laboratories, food manufacturers, ingredient suppliers, analytical service laboratories, and University research laboratories determine the chemical composition and physical characteristics of foods for various purposes. Method selection is usually based on the purpose of the analysis, the characteristic and validity of the method, and the food matrix involved Important: Philippine National Standard (PNS) are followed if the product is within and consume in the Philippines Codex Alimentarius is the minimum standard lang so it can fit globally, kaya if the product is to be exported to other countries need din i-consider yung standard nila PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 7  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 sample, and that can be L2: Samples and Sampling Method individually described, analyzed, or combined. B SAMPLING Selection of a set of elements from a target population or product lot Allows you to draw conclusions or make inferences about the population or product lot from which the sample is drawn B2 GOAL OF SAMPLING Obtain a fraction of the whole material D. Laboratory Sample for analysis that is manageable and Fraction of the primary sample reasonably economical, yet still that is actually used in the final laboratory analysis representative of the whole. E. Analytical/Test Sample IMPORTANT CONCEPTS IN SAMPLING The portion prepared from the laboratory sample, from which A. Population the portions for analysis are Totality of the items under taken consideration; the whole of the material whose properties we are trying to obtain an estimate of B. Sample Subset of a population made up of one or more sampling units; a group of items taken from a larger population to give information about that population. it can also serve as a basis for decisions on the population or the process that created it. C. Unit Each distinct, identifiable unit of food that can be removed from the population as a PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 8  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 portions to be removed from the lot as F. Representatives The extent to which the sample samples. exhibits the same Must be design in such a way that the characteristics as the resulting data will contain a population representative sample of the parameters of interest and follow for all questions, as stated in the goals to be answered Attribute Sampling Variable Sampling To decide on the To estimate acceptability of a quantitatively the population based on amount of a the presence or substance or a absence of a certain characteristic on a characteristics in the continuous scale sample B4 ACCEPTANCE SAMPLING PLAN A set of rules by which a lot is to be inspected and classified (CAC/GL 5 —2004) Plan which states sample size(s) to be used and the associated criteria for lot acceptance (ISO 3534:2) G. Randomness TYPES OF ACCEPTANCE SAMPLING PLAN The extent to which units are A. Single Sampling Plan given equal chances of being Most common and easiest plan taken as part of the sample to use accept/reject decision made by H. Selection Bias inspection of one sample of a Tendency for samples to differ specified size from the population as a result of a systematic exclusion of some part of the population B3 SAMPLING PLAN A predetermined procedure for the selection, withdrawal, preservation, transportation, and preparation of the PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 9  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 B. Double Sampling Plan product is very good. (google) Give a questionable lot another chance A second sample is taken if the B3 SAMPLING PLAN results of the first sample are not conclusive with regards to accepting or rejecting the lot Depends on: Purpose of the analysis Nature of measured characteristic Nature of the population Nature of the product Nature of the test method B3.1 SAMPLING PLAN ASSOCIATED WITH THE TYPE OF PRODUCT CHARACTERISTIC Type of Type of Sampling Plan Characteristics C. Multiple Sampling Plan Commodity ‘Attributes’ used when a manufacturer defects produces a large number of products. It involves taking Compositional ‘Variables with unknown several samples from a batch Characteristics standard deviation’ for and counting the number of normally distributed defective products in each characteristics and sample. (Google) ‘attributes’ for characteristics whose D. Sequential Sampling Plan distribution deviate are much like multiple–sample significantly from normal plans that involve, after every sample, deciding to either Health-related Specified sampling plans accept the lot, continue properties to be proposed sampling, or reject the lot, but, appropriate to each unlike multiple–sample plans, individual situation there is no predetermined finite number of samples to be made. (Google) NATURE OF POPULATION E. Skip Lot Sampling Plan A. Continuous population only a fraction of the submitted No physical separation lots are inspected. However between the different parts of this should only be used when the sample it has been demonstrated that e.g. liquid milk/oil in a tanker the quality of the submitted PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 10  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 B. Compartmentalized population Split into a number of separate sub-units e.g. boxes of food moving in a conveyor belt C. Homogenous population Properties of the individual samples are the same at every location within the material e.g. tanker of oil D. Heterogenous population Properties of the individual samples vary with location e.g. a truck full of potatoes, some of which are bad B5 NATURE OF TEST PROCEDURE Speed Precision Accuracy Cost per analysis Destructive or non-destructive More samples may be analyzed if the test procedure is capable of rapid, low cost, nondestructive and accurate measurements. PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 11  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 precision and avoidance of L3: Developing a Sampling Plan systematic sampling error C. Step 3: Indicate Sample Size C DEVELOPING A SAMPLING PLAN Sample size depends on: ➔ The expected variations in 1. Identify the parameters to be measured, properties within the range of possible values, and the population e.g humidity, required resolution initial moisture, content, location san galling ang 2. Design a sampling scheme sample, time of delivery 3. Indicate sample size ➔ The seriousness of the 4. Design data storage formats outcome if a bad sample is 5. Assign roles and responsibilities not detected - rejection should be lower than 5% ➔ Cost analysis C.1 STEPS IN DEVELOPING A SAMPLING PLAN Type of analytical technique used A. Step 1: Identify Parameters, Range of Values and Resolution C2 SAMPLE SIZE CRITERIA Goals will tell us what to measure and how Ranges help screen outliers Level of precision (labas o wala sa trend) Confidence level - below 5% unresolved. Resolution helps choose Therefore there is, no significant measurement equipment difference with the results Degree variability C3 ESTIMATING SAMPLE SIZE B. Step 2: Design Sampling Scheme A sampling scheme is a Census for small populations detailed description of what Imitating a sample size of similar studies data will be obtained and how Using published table this will be done Applying formulas Principles that will guide the design of a sampling scheme: PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 12  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 C4 SAMPLING METHODS A. Probability Sampling Methods Probability Non-Probability Involves Does not random involve random selection selection Every item has No assurance a chance of that each item being selected has a chance of Exact being selected population Exact size is KNOWN population and all the size is elements or UNKNOWN B. Non-Probability Sampling Methods members are and all the Subjective and no system listed in a elements or sampling members followed frame cannot be listed C5 NON-PROBABILITY SAMPLING TO USE OR NOT TO USE CLASSIFICATION OF SAMPLING TECHNIQUES Quota sampling - if nakakuha ka na ng sample na nagsasatisfy sa requirements Snowball sampling - used a rare sample or found only in remote areas PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 13  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 C6 SAMPLE PREPARATION Physicochemical properties of the sample Fineness of the homogenized material Removal of Extraneous Matter Capacity Sample homogenization Throughput Sample Size Reduction Prevention of Changes in Sample Sample Identification SAMPLE HOMOGENIZATION C6.1 REMOVAL OF EXTRANEOUS MATTER Physical methods Chemical Methods Enzymatic Methods Extraneous matter are the contaminants present in the sample. Washing or wiping Separation of core and outer and inner tissues Filtration or Centrifugation Draining C6.2 SAMPLE HOMOGENIZATION SAMPLE HOMOGENIZATION A. Physical methods inter-unit variation Stirring ➔ Variations in the properties of Tumbling Grinding different units within the sample ➔ Coarse grinding (5mm): intra- unit variation: Jaw breakers, cutting mills ➔ Variations within the individual ➔ Fine grinding (65um): units in the sample Planetary ball vibration mills, mills; CryoGrinders Physical or chemical treatment by which Shearing: Blenders, the composition and structure of a rotor-stators, and some glass substance or mixture of substances is homogenizers made uniform Beating Shocking: Sonication - uses sound waves and used in liquid sample B. Chemical Methods Strong acids - for titration Strong bases - for titration SELECTING SAMPLE Surfactants/Detergents HOMOGENIZATION TECHNIQUE ➔ Ionic: anionic and cationic ➔ Non-Ionic Material of homogenization device C. Enzymatic Methods Cleaning requirements Proteases Quantification of organic compounds Cellulases PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 14  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 Lipases These enzyme cut the bonds to make it linear or homogenize Quartering method same as serial dilution or one log reduction cycle C8 PREVENTING CHANGES IN SAMPLE C7 SAMPLE SIZE REDUCTION Control of microbial growth A small more manageable portion is Inactivation of enzymes selected for analysis (laboratory sample) Lipid protection Sampling plans often define the method Prevention of physical changes for reducing the size of a sample in order to obtain reliable and repeatable C9 ENZYME INACTIVATION results e.g moisture content should be same and accurate Freezing Drying SAMPLE SIZE REDUCTION Chemical preservatives Heat treatment A. SPOONING Irradiation Used in powder products Ohmic heating involves random insertion of a spoon or another sampling Pulse electric field device into the previously Ultrasonication ground and sieved sample Cold Plasma High hydrostatic pressure C10 FACTORS TO CONSIDER FOR CHOICE OF ENZYME INACTIVATION TREATMENT B. SCOOPING a scoop of homogenized material is divided among 4 Size, consistency, and composition of the containers material Enzyme present Intended analytical determination PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 15  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 must be designed to ensure proper sealing and preclude unauthorized handling C14 SAMPLE STORAGE Refrigeration or freezing for chemically C11 LIPID PROTECTION unstable samples Refrigeration preferred for storage of samples for timescales of hours to day Store samples under nitrogen or some Freezing should be avoided when storing other inert gas unstable emulsion Store samples in dark rooms or covered Preservatives can be added to stabilize amber bottles are light sensitive certain food substances use if not so Store sample in refrigerated temperature critical or stable naman ang food Addition of antioxidants, provided they do not interfere with the analysis C15 SAMPLE IDENTIFICATION C12 PREVENTING PHYSICAL CHANGES Proper labelling of samples ➔ Sample description Physical changes in a sample include: ➔ Time sample was taken ➔ Water lost due to evaporation ➔ Location sample was taken from ➔ Water gained due to condensation ➔ Person who took the sample ➔ melting or crystallization of fat ➔ Methods used to select the sample ➔ structural properties may be Keep a detailed notebook clearly disturbed documenting the sample selection and Prevent the changes by controlling preparation procedures performed and sample temperature and the forces recording the results of any analytical (environment) that it experiences procedures carried out on each sample Mark each sample with a code on its C13 SAMPLE CONTAINER label that can be correlated to the notebook Must not be affected by the sample Important: It is possible to use combined must ensure that the integrity of the sampling technique like probability and non probability sampling sample is maintained must be designed and manufactured in a way that allows leak-proof or air-tight closure must be strong enough to withstand transport and storage PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 16  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 L4: Data Analysis D4 MEASURES OF ACCURACY Absolute error - difference between the D1 MEASURES OF CENTRAL TENDENCY measured value and the true value Mode is most frequent number Median is middle number in list 𝐸𝑎𝑏𝑠 = 𝑥 − 𝑇 Mean is average of all numbers Where: 𝑥 = experimentally determined value T = true value The absolute error term can have either a positive or negative value Relative error - absolute error divided by the true value 𝐸𝑎𝑏𝑠 D2 RELIABILITY OF ANALYSIS 𝐸𝑟𝑒𝑙 = 𝑇 May be expressed in percent, parts per Accuracy - How close to the “real” value thousand, or parts per million depending on Precision - How close are the individual the magnitude of the result measurements to each other MEASURES OF PRECISION A. Range Difference between the largest and smallest observation Not too useful and thus is seldom used in data evaluation Ginagamit lang kadalasan ang range sa pag me-measure ng outliers. B. Standard deviation (SD) D3 DETERMINING ACCURACY Measures the spread of the experimental values Using known samples (standards) from Σ (𝑋𝑖 − 𝑋) 2 institutions, like the National Institute of 𝑆𝐷 = 𝑛−1 Standards and Technology, and checking the assay against these samples Use data from other laboratories Use data from literature PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 17  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 Values of T for various levels of certainty Degree of Levels of centainty Freedom (n-1) 95 % 99 % 99.9 % 1 12.7 63.7 636 2 4.3 9.93 31.60 3 3.18 5.84 12.9 4 2.78 4.60 8.61 C. Coefficient of variation (CV) 𝑆𝐷 5 2.57 4.03 6.86 𝐶𝑉 = 𝑥 100 𝑥 As a rule, a CV below 5% is considered 6 2.45 3.71 5.96 acceptable 7 2.36 3.50 5.40 D. Confidence Interval (CI) The true value will fall within 8 2.31 3.36 5.04 this interval at a specified confidence level 9 2.26 3.25 4.78 10 2.23 3.17 4.59 For large numbers of sample: 𝑆𝐷 𝐶𝑙 = 𝑥 ± 𝑧 𝑣𝑎𝑙𝑢𝑒 𝑥 Mostly ginagamit sa food industry is yung 95 % 𝑛 kasi we always a lot 5 % margin of error For small numbers of sample: E. Standard Error of the Mean (SEM) 𝑆𝐷 𝑆𝐷 𝐶𝑙 = 𝑥 ± 𝑡 𝑣𝑎𝑙𝑢𝑒 𝑥 𝑆𝐸𝑀 = 𝑛 𝑛 Values for Z for checking both upper and Tells how many units away is the lower levels sample mean from the population mean Degree of Certainty Z value A property of the mean and not of the (confidence) observed sample 80% 1.29 90% 1.64 TYPES OF ERRORS 95% 1.96 A. Systematic Errors (Bias) Produce results that 99% 2.58 consistently deviate from the expected value in one direction 99.9% 3.29 or the other Affects accuracy of the result PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 18  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 Source of error is often difficult miscalibrate to identify instruments Often involves inaccurate Use of instruments instruments or measuring under conditions where devices. errors are large (e.g., acid error; alkaline error) Method Errors - Arise from non-ideal chemical or physical behavior of analytical systems. ○ Sources: Non-ideal chemical or physical behavior of the reagents and reactions on which an analysis is based Slow or incomplete reactions Instability and lack of specificity of reactants/reagents One reason or source of error is yung hindi Side reactions calibrated ang instruments or measuring devices. Matrix Effects Interfering substances Incorrect wavelength Types of Systematic Errors and uncorrected blank Personal errors - result from carelessness, inattention, or personal B. Random Errors limitation of the experimenter Arise from uncontrollable or ○ Sources: indeterminate variables Operator Bias Always present in any Number Bias analytical measurement Lack of proper Fluctuate in a random fashion knowledge and skills of Essentially unavoidable analyst Affects measurement precision Color-blindness Usually small in magnitude ○ Sources: Instrumental errors - caused by Instability of imperfection of the measuring instruments instrument, faulty calibrations, use Environmental under inappropriate conditions, or fluctuations non-ideal instrument behavior. Inconsistent ○ Sources: operator skill Glassware holds or Poor reagent delivers volumes slightly control different from those Sample indicated by their variability graduations Blank variability Uncalibrated or Faulty variability PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 19  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 Replication Variable gains Recovery experiments and losses Reference samples Confirmatory analysis C. Gross Errors (Blunders) Collaborative testing Made by accident and can occur at any point Often product of human errors D6 PROPAGATION OF ERRORS (human negligence) Often easily detected and eliminated Addition (Z = X + Y) & Subtraction (Z = X - Lead to outliers Y): ○ Sources: 2 2 ∆𝑍 = ∆𝑋 + ∆𝑌 Eqn 1 Wrong sample, calibration, Multiplication (Z = XY) & Division (Z = X/Y): sampling, method, reading, ∆𝑍 = ( ∆𝑋 ) 2 Eqn 2 transposition 𝑍 𝑋 and transcription reagent ∆𝑋 is the standard deviation of the mean value X ∆𝑌 is the standard deviation of the mean value Y Losses ∆𝑍 is the standard deviation of the mean value Z Inattention to detail Lack of statistical D6.1 PURPOSE OF ERROR PROPAGATION control Contamination Quantifies precision of results Wrong calculations Identifies principal source of error and suggests improvement Justifies observed standard deviation D4 ERROR CONTROL Identifies type of error Know what is right and do it If \𝑍𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 − 𝑍𝑙𝑖𝑡𝑒𝑟𝑎𝑡𝑢𝑟𝑒\ ≤ ∆𝑍 then error is random Gain experience in analysis If \𝑍𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 − 𝑍𝑙𝑖𝑡𝑒𝑟𝑎𝑡𝑢𝑟𝑒 \ >> ∆𝑍 then error is Develop proper attitude systematic Focus (presence of mind) D7 REJECTING DATA During laboratory procedure, dapat ma take-note mo yung mga nangyayari. You must know what are the possible reason why hindi nakuha ang Q-test - commonly used to decide tamang results. whether an experimental value can be rejected or not. D5 WHAT IS RIGHT? 𝑋𝐵𝐴𝐷 − 𝑋𝑁𝐸𝑋𝑇 𝑄𝑣𝑎𝑙𝑢𝑒 = 𝑋𝐻𝐼𝐺𝐻 − 𝑋𝐿𝑂𝑊 Quality of glassware Where: XBAD = questionable value Handling and cleanliness of equipment XNEXT - next closest value to XBAD Use of blanks XHIGH - highest value of the data set PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 20  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 XLOW - lowest value of the data set sample If the Q-value > Q-test table for the The standards are used to number of samples being analyzed, it establish the instrument can be rejected calibration function (instrument response as a function of the known analyte Table of Critical Q-Values for a 95% Confidence concentration. Level The calibration curve should be Number of Q-value for Data linear in at least the range of Observations Rejection the analyte concentrations The linear relationship is used 3 0.970 to predict of an unknown analyte solution 4 0.829 The linear relationship between the analyte concentration and 5 0.710 response is established using tests for association 6 0.625 The equation of the line used for predicting the 7 0.568 concentration of the unknown analyte is established using 8 0.493 regression analysis 10 0.466 D8 TEST FOR ASSOCIATION QUANTIFICATION METHODS Correlation Coefficient A. Comparison with Standards Defines how well the data fit to a straight Direct Comparison line ○ It involves comparing a Σ (𝑋𝑖 − 𝑥) (𝑌𝑖 − 𝑌) property of the analyte 𝑟 = 2 2 with standards to see if Σ( (𝑋𝑖− 𝑥) Σ( (𝑌𝑖− 𝑌) they match or closely Values are usually reported to at least 4 align. (summarize) significant figures Titration method ○ The analyte reacts with -1.000 ≤ r ≤ 1.000 a known reagent In analytical work, r ≥ 0.9970 (titrant) in a stoichiometric ratio, and R can be computed through excel the titration continues until a color change or instrument signal Coefficient of determination (r2) shows the reaction is Gives better perception of the straight complete. (summarize) line even though it does not indicate direction of the correlation B. External Standard Calibration Series of standard solution is prepared separately from the D9 ERRORS IN EXTERNAL STANDARD PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 21  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 CALIBRATION detected standard comes only from the added amount. (summarize) Matrix effect Examples: Difference in experimental variables at the times at which blank, sample, and standard are measured Contamination during sampling or sample preparation Incorrect standard preparation Difference of the chemical form of the standards to that of the analyte D10 TEST FOR DIFFERENCE Inadequate number of standards for the calibration curve Two populations ○ T-test determine the significant QUANTIFICATION METHODS difference of the mean of the C. Standard Addition Method experimental group versus the Useful for analyzing complex mean of the control group samples in which the likelihood Three populations: ANOVA and DMRT of matrix effects is substantial ○ ANOVA - Analysis of Variance; The sample is “spiked” with a determines differences between known amount or known many sets of data amounts of a standard solution of the analyte ○ DMRT - Duncan’s Multiple Range Types: Test; done when at least one ○ Single-point standard sample is significantly different addition from the others; DMRT can ○ Multiple additions identify which sample(s) is/are significantly different D. Internal Standard Method A known amount of reference species (internal standard, IS) is D11 REPORTING RESULTS added to all the samples, standards, and blanks Mean, standard deviation, CV, standard Ratio of the response of the analyte to that of the IS vs. error analyte concentration Reported to correct significant figures Compensate for errors that using proper rounding rules influence both the analyte and the IS to the same proportional extent. D12 SIGNIFICANT FIGURES RULES The signal should closely resemble the analyte's but be different enough for the instrument to distinguish them. It must also be absent from the sample matrix, so any PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 22  FOOD ANALYSIS FOTE 202 1ST SEMESTER – PROF. ERWIN DESCALLAR – W/S 13:30 - 17:30 measurements. For a single measurement of one quantity, the uncertainty is generally determined by the instrument used. ○ The pan balance reads to 0.01 g, so an example data would be: 25.10 ± 0.01 g ○ The analytical balance reads to 0.0001 g, so an example would be: 25.1000 ± 0.0001 g ○ A 100-mL graduated cylinder might read to 0.5 mL, so examples are: 25.0 ± 0.5 mL or 31.0 ± 0.5 mL D13 ROUNDING NUMBERS RULES If the number to be dropped is greater In a logarithm of a number, keep as than or equal to 5, increase the number many digits to the right of the decimal to its left by 1 point as there are significant figures in If the number to be dropped is less than the original number. 5, there is no change In an antilogarithm of a number, keep Postpone rounding until the calculation as man digits as there are digits to the is completed right of the decimal point in the original number. The number of significant figures used in reporting a final result determined by the uncertainty (SD) of the PAGE NAME OF OWNER – BSFT 2-1D – CONTACT INFORMATION 23