Nghiên cứu sữa part 6

3 The expertise can be made more widely available The expert's time can be saved Routine decisions delegated to an expert system allow the human expert to concentrate on the more abstract, currently developing problems It takes years for human experts to learn their specific skills Expert systems can be copied on magnetic media in seconds or minutes Humans become sick, retire, and die Expert systems continue to work consistently and predictably Human expertise is expensive Savings can be realized on maintaining and updating the knowledge base Software maintenance is a large cost of software systems over their lifetime Much of the programming time is spent in finding and adjusting for modification side effects on programs The emphasis on structured analysis and programming techniques in software development because of deadline pressures detracts from the goal of a more structured approach The result is that a less than ideal code is generated The clear distinction of facts, heuristics, and inferencing knowledge in knowledge-based systems reduces maintenance and update costs because effects of changes are restricted Disadvantages of expert systems that can be considered are as follows: When considering cognitive activities to other human tasks, expert systems are good at extracted, cognitive, logical thinking They are not well suited for managing highly sophisticated sensory input or mechanical motor output Expert systems exhibit a narrow band of simulated intelligence based on a narrow range of codified, heuristic knowledge They not respond well to situations outside their range of expertise Expert systems are weak in common-sense knowledge Human reasoning uses associations, which may not be appreciated or even realized when developing the knowledge base These associations and thought processes are based on a range of contextual information including social surroundings, random memories, feelings, emotions, and other nonrational information Even more difficult to capture is human intuition In this case, humans draw spontaneously from their subconscious of creativity and insight If a decision maker goes by hunch more than by facts or logical arguments, the problem is not appropriate for an expert system It is difficult for an expert system to learn unless through the human user or knowledge engineer 3.3*2 Knowledge Acquisition Expert systems rely greatly on knowledge In order to obtain quality knowledge for implementation of a successful expert system, the following elements should be considered: application selection, domain expert selection, knowledge engineer selection, tool selection, knowledge acquisition, and system development and deployment A useful technique in developing a list of potential expert system applications is to observe any knowledge bottlenecks These can be recognized as people waiting for others to m&ke decisions and people stopping to search for needed information Typically, small expert systems are used as intelligent job aids Most expert systems in use today are of this size They accomplish small procedural or diagnostic functions such as equipment maintenance or product defect analysis An expert system can be selected based on its ability to assist, accelerate, or improve the quality of decision-making in groups already using computers Midsize expert systems are designed to be installed on mainframe computers In addition to the functions of the small systems, most midsize applications include diagnostics and large configuration and scheduling systems Large expert systems have traditionally been developed in LISP and on LISP machines They capture large amounts of expertise and make it widely available through the organization Valuable heuristic knowledge that might be lost through retirement is often an appropriate candidate for an expert system application On-line operators could take advantage of expert knowledge when the human expert is not available However, the task should be well defined and narrow The domain is the area of knowledge or expertise being captured A knowledge domain expert will typically have 10 or more years of experience If possible, an individual should be sought who can analyze a problem and explain his or her thinking in specific terms He or she should have the ability to analyze problems systematically A good working environment needs to be created In addition, the domain expert needs to be both knowledgeable and cooperative The role of the knowledge engineer is to research existing knowledge and to help the expert describe his or her own problem-solving procedures The knowledge engineer needs to understand symbolic programming techniques Interpersonal skills are also a necessary attribute The expert system tool should be selected in terms of the type of problem to be solved Types of systems include diagnostic, training, and decision support More specific information of tool selection will be covered in Section 3.3 The knowledge acquisition process involves gathering recorded relevant information and preparing it for entry into the computer Knowledge can be represented in several ways including frames and rules Initially a prototype is constructed to demonstrate the basic operations Refinements can then improve accuracy, completeness, and user-friendliness The prototype is then field tested to verify its accuracy and usefulness Users can document all of their decisions in the domain area for a specified time period The period should be long enough to include multiple occurrences of most events in the system When the system is operating in an on-line situation, it should be taken off-line and presented with the same problems The answers should be in agreement An important factor in successful implementation of the system is user training Users' concerns and needs should be anticipated Continued support should be available as new knowledge accrues and new needs develop 3.3.3 Tool Selection The term tools refers to expert system software Shells are developmental tools that provide users with a framework within which to build their knowledge base The alternative is to use a symbolic language such as LISP First-generation tools were written in LISP, but eventually tools were designed to integrate with existing hardware and software Languages are more flexible than shells, but more difficult and time consuming In order to match the tool to the problem, it is necessary to know problem requirements and the types of knowledge representation and inferencing strategies a particular tool does well In selecting an appropriate tool, both the shell and the interfaces need to be considered Although the shell may allow the desired knowledge development, if it is unable to interface with the process and existing software, its practical application will be limited Debugging aids and technical support are additional factors Expert systems can be categorized into four classes: specialized tools, smaller tools, middle-range tools, and sophisticated tools.5 An example of a specialized tool is PICON [LISP Machines, Inc., (LMI), Andover, MA, U.S.A.] PICON is used in process control equipment and in complex applications such as weather forecasting and financial market monitoring Examples of smaller tools are VP Expert (Paperback Software, Berkeley, CA, U.S.A.) and Magellen (Emerald Intelligence, Ann Arbor, MI, U.S.A.) Generally, these tools use a single representation scheme and are designed for lower priced microcomputers Middle-range tools include products such as GURU (Micro Data Base Systems, Inc., Lafayette, IN, U.S.A.) and KEW & KESII (Software Architecture and Engineering, Arlington, VA, U.S.A.) Sophisticated tools such as ART (Inference Corporation, Los Angeles, CA, U.S.A.) and KEE (Intellicorp, Mountain View, CA, U.S.A.) employ multiple knowledgerepresentation schemes and advanced graphical display mechanisms The basic design of expert system shells centers around the knowledgerepresentation structures and inference mechanisms The basic schemes are If/Then production rules, frames and networks, objects, and access triggers or demons Most products represent knowledge using If/Then production rules as the primary scheme Goal-directed, backward chaining is the simplest form of If/Then production rule representation and inference mechanism, followed by forward chaining combined with backward chaining Some systems support only one method at a time whereas others support a mixed mode The number of premise conditions allowed for each rule is sometimes limited To avoid complicated logic or decision trees, modular development capability is required This may be referred to as rule sets, knowledge modules, sections, state objects, or frames Other factors to consider in tool selection are support of uncertainty, rule sequencing procedure, user interface, help facilities, knowledge acquisition features, access to other programming facilities, capacity and response time, hardware requirements, pricing, and vendor support The demands on a well utilized expert system normally increase, so expandability is important Also expert system tools consume large quantities of RAM and CPU MIPS This is particularly the case with the middle-range and higher systems Larger systems are referred to as environments Large-scale environments provide a wide range of development and execution utilities They have multiple programming and support capabilities Some of the features include utilities for design of custom windows and mouse-sensitive menus, debugging tools, multitasking services, libraries of routines, and mathematical and statistical functions 3.4 Expert Systems and Process Control Before discussing the application of expert systems to process control, a description of traditional computer process control is useful This type of control is in widespread use and expert systems generally build on or integrate with traditional systems Athough promoted expert systems are being applied to an increasing number of manufacturing processes, reports of applications actually in use are few Problems that have hindered their progress will also be discussed in the following sections 3.4.1 Preexpert System Developments Although the study of dairy processing often centers around specific classes of dairy products, there are often common operations involved such as cooling, heating, or mixing A systematic approach to the study of dairy processing is to examine these common or unit operations A unit operation accomplishes some specified function on a product such as heating, cooling, pumping, mixing, evaporating, dehydrating, separating, and cleaning The control of process parameters such as temperature and pressure was originally maintained normally by human operators as they observed gauges and sight-glasses In the 1940s pneumatic instrumentation was developed, which was able to sense process parameters and provide feedback control Signal transmission to a remote control room was possible The control equipment usually remained with the piece of equipment or was routed to a control panel Pneumatic instrumentation is still used in plant control equipment Its longevity is due in part to its high reliability These systems are based on single-loop control throughout a plant Single loops involve one measurement, one control algorithm, one actuator, and one process variable For instance, the function may be to maintain a temperature or a particular flow rate Feedback control is observed based on the difference between the measurement and a specified setting or set-point In the 1960s digital computer usage began in process control Analog instrumentation became available, and electronic instrumentation became more stable and repeatable than comparable pneumatic controllers Signals could be transmitted over longer distances With traditional control, operating conditions are predetermined to maintain certain levels or temperatures Programmable logic controllers (PLCs) replaced many of the discrete relays Computer-based control systems were much more flexible than traditional hard-wire, relay-logic control systems Widespread adoption of the technology was slow because of concern over massive problems due to a single computer failure By taking advantage of PLCs in distributing computing over a wider area, concerns over large-scale failures were alleviated Digital-controlled systems are widely used control mechanisms in use today Once computer-based control systems are interfaced with sensors, valves, and switches, the measurement devices can provide input for the computer while signals from the computer provide input for the control device A key feature in this system is the ease with which process changes can be made by reprogramming the computer Computer-based process control begins with the monitoring and control of unit operations These include blending, pumping, heating, and storage operations as well as clean-in-place (CIP) operations Blending or formulation can be considered as a unit operation For instance, computers can be used in standardizing cheese milk to give the correct casein/fat ratio Yield prediction formulas can be used in the calculations The 44ASEA Master Batch" control system for process control in ice cream manufacturing uses programs comprised of modules from an integrated software library.6 Batch movement and formulation are fully automated Quantities of raw materials and finished product are tracked and reported Blending operations using least cost formulation and computer-aided optimization are well established in the food industry Blending operations must also consider legal and sensory requirements Metering of milk and other ingredients is often computer controlled The casein/ fat ratio for cheesemilk can be determined on-line using an infrared multicomponent analyzer The speed of the :ream meter is adjusted as needed Pumping systems have been designed to avoid problems associated with centrifugal pump cavitation The systems include a PLC, a sound sensor, and a variable speed drive at the pump motor The sensor can detect preliminary cavitation impulses, signaling the variable speed drive to adjust the pump's revolutions.7 During pasteurization, a computer can monitor all of the parameters of the process These include temperatures, valve positions, liquid levels, and feed rates Some control systems are limited to data acquisition without real-time control ability In these cases, process control adjustments are still dependent on the operator A retort management system has been developed (TechniCAL, Inc., Metairie, LA, U.S.A.) that provides temperature and pressure control The system monitors these signals along with all facets of retorting including cooking, venting, and cooling If a temperature deviation occurs, the system automatically recalculates a new process time and makes all necessary adjustments USDA and FDA officials were involved in developing and implementing the system to ensure regulatory compliance Controllers and recording devices for dairy pasteurizers must also meet federal and state health codes Instrumentation for these applications includes differential pressure controller and single- and dual-point diversion recorder/controllers The pressure controllers measure and indicate pressures at the raw product inlet of a hightemperature-short time (HTST) pasteurizing regenerator The diversion recorder/ controllers specify control temperature levels Liquid levels in tanks can be monitored simultaneously The data can be used in production tracking and inventory control Enclosed cheese vats are well suited for automated control Ingredient addition, temperature control, cutting, stirring, draining, and washing are commonly computer controlled CIP systems have long been established as major computer process control systems in the dairy industry Recent developments include computer-controlled detergent dosing systems, environmentally friendly CIP systems, automatic analyzers, and CIP data logging systems Some difficulties encountered in automation and computer utilization in the food industry are a lack of suitable sensors, low profit margins, use of batch/continuous operations, and the installation of equipment that is not integrated into the whole process Beyond unit operations, these systems currently can supply data to a higher level host computer for further data manipulation Devices can be mixed and matched and integrated into plantwide control schemes The use of single loop controllers involves only configuration without dedicated software Configurable software is used to sequence control systems so that the process operates as a sequential series of linked operations that can operate independently of each other (fill tank, empty tank, sterilize, etc.) but are still related in terms of order and integrity to process operation To design suitable control systems around these concepts, all tasks must be uniquely defined and self contained and the plant must be divided into areas of unit operations The distributed operator interface can then use a single coaxial cable to connect modules rather than thousands of strands of wire For example, consider diverse processes such as manufacturing regular, flavored, and evaporated milk; storing products; and cleaning The status of the process is shown by means of matrices at the operator station Some of the linked computers control manufacturing while another acts as a management computer With a computerized control system, the desired functions can be monitored and the system programmed for the next product Computers can chart equipment conditions and locate developing problems at any point in a process Products such as the System 30 (APV Crepaco, Inc., Chicago, IL, U.S.A.) are useful for small plants yet allow for expansion to scan and control thousands of sensors and actuators and combine with many different users Features of the System 30 include the ability to network with other systems; to communicate with a wide range of protocols including intelligent sensors, bar code readers, PLCs, and PC software packages; and to provide fault-tolerant operation 3.4.2 Expert System Applications Application of software-based automation to a process plant results in much less repetitive manual adjustment Automation requires clearly defined algorithms and the appropriate design, installation, and maintenance of sensors, controllers, and actuators Included in the form of software is much of the operator's knowledge and expertise in management of the process This almost suggests a sort of preexpert system Process control software is able to monitor many operating, trend, and alarm parameters Networked with a host computer to make process information available to appropriate personnel, the necessary information system is in place to permit online, expert-system applications Placing microcomputers in plants for local solutions is useful However, production operations require changes in programs and few qualified technicians are available to make changes when problems occur Documentation is often incomplete Expert systems can be used to take the place of experts or qualified technicians Three types of expert systems used in process control are self-tuning controllers, control system configurations, and fault analyzers Self-tuning controllers automatically change controller settings based on control loop performance Proportional-integral-derivative (PID) refers to a three-mode algorithm for this type of controller PID controllers use either pattern-recognition or frequency filters With pattern-recognition, responses are characterized for overshoot, damping, and period, and corrective adjustments are made Frequency filtering achieves control by forcing the outputs of two filters to conform to a given ratio Using pattern recognition, a controller can identify a disturbance response in the loop-error signal When the loop error exceeds a specified threshold level, it is tested against a series of rules The information obtained is used for a tuning calculation Another type of self-tuning controller is model based These controllers are adjusted according to the difference between the process response and a model used for comparison Control system configurators are used to connect components together to form a control system With many controlling devices interacting, the optimal configuration becomes important Expert systems are able to select optimum configurations for control systems The user can enter known operating conditions and process parameters and the program will select the configuration that best satisfies the requirements Expert systems can provide steps on configuration of the system Connecting many I/O points with accompanying information can be accomplished more easily The system can be regularly checked for accuracy with on-line help provided as required Expert system configurators can simulate data in order to test a system prior to actual operation Therefore, errors in control or other functions can be observed and corrected Fault analyzers diagnose problems as they arise and provide corrective instructions to operators In serious failure conditions an expert operator may not be available or may have insufficient time to respond An expert system can be used to interpret a series or pattern of alarms quickly, resulting in a description of the fault and a recommendation for corrective action On observing an out-of-control situation, an operator must determine the cause of the situation and the best solution The knowledge of how one or more experts would respond could be obtained from a properly designed expert system The knowledge base can be continually updated by human experience and by information the computer gains as failures occur Several advantages the expert system has are the depth of experience coming from several human experts and the much larger amount of data and factors an expert system can assimilate that is available to the user Personal computer (PC)-based systems can be networked to provide performance levels equivalent to much larger machines Coprocessors are additional processors used to speed up operations by handling some of the duties of the main processor or CPU Coprocessor technology in the PC allows the accessing of real-time information PLCs can transfer data directly into a monitoring PC, which can run an expert system based on those data A user can define conditions or rules that are continuously checked If conditions are found to be true, certain functions are performed For example, a cluster of valves may be checked to see if all are closed If all valves are closed, the status of the calculated point is false If a valve is open, the status is true Another rule sends a start command to the pump if a valve is open or a stop command if all valves are closed This rule based arrangement, sometimes referred to as an event processor, allows maximum flexibility in developing control strategies The processing capability of computer-based controllers can be increased with the integration of expert systems Expert systems can provide an intelligent interface to the controlling device or sensor Many process variables can be examined and assimilated However, integration of expert systems with a process control system is complicated by the real-time interaction requirements Not all tools are useful for this purpose, and the language constructs become critical For these reasons incorporation of expert systems into on-line process control has been slow to develop Some of the problems associated with expert system integration have been addressed by workers at Honeywell, Inc resulting in their expert systems development and delivery environment called TDC 3000 Expert The development of many successful expert systems has centered mainly around small-scale prototypes Some of the requirements may differ with medium and largescale systems In the larger systems, integration with the existing data structures is required To build intelligent applications of scheduling, simulation, supervision, and statistical process control, integration of management information systems and database technology with expert systems methodology is critical To successfully integrate expert systems into a process control environment, the expert system must access the real-time manufacturing data and communicate with the human operator The inference engine instructs the data accessor Because a large number of variables are probably being referenced, and data points are changing often, access needs to be timely Otherwise the data collection can interfere with the reasoning process and reduce the pertinence of the system's advice The inference engine keeps track of what data are needed and then instructs its acquisition while the knowledge base is inactive In providing information to the operator, the expert system must prioritize results as to their importance Specific declarative and procedural mechanisms allow the expert system to reason about how intelligent it can afford to be and still provide a timely response Language constructs need to support change and trends Procedural representation should be avoided For a knowledge base, input is needed from expert and process engineers about normal plant behavior as well as problem situations What constitutes a problem depends on the state of the process For example, what constitutes high pressure during the cheesemaking set is different than high pressure during stirout of the curd A slight problem with temperature control adjustment on an enclosed cheese vat may be less critical than an out-of-position discharge valve 3.4.3 Knowledge Representation in Process Control Classes of objects and their attributes can be used in a knowledge base to track normal behavior of a plant Various classes of objects are defined by name Members of a class have various attributes For example, a class named vats could have the attributes pressure, diameter, or phase-of-process Classes may inherit attributes or they may be defined for them Context-sensitive rules change their own values and make concomitant assertions about values of other variables in the system Some classes and attributes can be used in a knowledge base to represent and track the normal behavior of a plant For example, filling a vat covers different phases These all belong to the phase attribute Each context of the phase attribute provides rules for recognizing when a process for the vat has moved to the next phase To track abnormal or undesirable behaviors, the expert system needs (1) conditions from which problems conceivably can arise; (2) evidence that can confirm or rule out the actual existence of a problem; (3) descriptions of other problem situations, if there are any that can possibly be causes of the problem; and (4) actions of the operator to remedy the problem If a knowledge base contains information to identify a problem, there must be a structure to the knowledge so that the firing of a rule or frame leads to the application of other related knowledge Knowledge could be represented using rules or general purpose frames, but those techniques have disadvantages An inherent structure is needed in the knowledge base to identify the reasons for a process upset Using rules, this structure is not evident At run time the inference engine would not know the boundaries of its own knowledge In a dynamic, real-time application, a simple forward or backward chaining inference engine cannot distinguish between a lack of rules resulting from the temporary absence of pertinent data and lack of rules because of an inadequate rule base A second and related disadvantage to the use of general purpose knowledge representation techniques is that a lack of knowledge structure makes selective activation and deactivation of knowledge at run time very difficult With generic rule or frame representations, the different criteria for ordering advice-giving knowledge are difficult to distinguish A knowledge base written as generic situation-action rules requires more development and maintenance effort by the knowledge engineer Frames can be organized into specialized structures called situation frames Slots are available for holding knowledge Slots connect with other frames in knowledge base links An inference engine can traverse cause-effect structure from top down and can issue operator advisories The human interface aspects of integration need to be considered Collaborations should take place with the operator An exhaustive search for all possible undesirable process situations must be performed As not all evidence can be obtained from instrumentation, what is practical needs to be decided Redundant and trivial nuis- ance messages, which the operator already knows, must be eliminated The operator needs time to observe, comprehend, and act The operator needs to access process data in the knowledge base using terminology with which process engineers are familiar Reference needs to be made to trends in process data over various time intervals Trend intervals and statistics can be predetermined and precomputed into the system Historical buffers of raw data can be maintained and made available for the engineer's questions Ways to interact with an operator in real-time application need to be provided Occasionally, the system cannot get information from the data and needs to query the operator However, an operator may not be there, and the system cannot stop A special class of query objects can be treated for which instances are declared An instance can be when an answer attribute is needed to evaluate the expression, the latest answer is unknown, and the corresponding question is not displayed The evaluation then causes the text of the question to get delivered to the operator's console The main decision is to determine what kind of explanation capability should be provided and what information operators will find useful The expert system could explain why the problem exists, how to perform the recommended action or how to obtain the information needed, why the action should be performed and how it will help, and how the conclusion was determined or the line of reasoning 3.4.4 Commercial Examples In developing industrial touchscreen workstations, a graphic display is configured using "If/Then/Else" statements and English language commands (Nematron, Ann Arbor, MI, U.S.A.) The G2 real-time expert system has been implemented in a number of process control situations including chemical process control, flight monitoring, network management, manufacturing, simulation, training, energy management, robotics, and water treatment G2 uses object-oriented representation of plant equipment and models of process behavior Heuristic and analytical knowledge is used Heuristics are a simplification tool to reduce the search in a large problem space To avoid time constraints, the system uses metaknowledge to focus the inferencing resources Metaknowledge involves rules acting on rules to reduce the search space The application developer can create classes of objects or import preexisting object classes from G2 An integrated simulator allows the developer to test an application prior to its deployment After the application is developed, tested, and deployed on-line, G2 can communicate with control systems, PLCs, databases, or other sources of real-time data INFI 90 (Baily Controls, Wickliffe, OH, U.S.A.) is entitled a strategic process management system This process control system is able to access embedded expert systems Referred to as EXPERT 90, the expert system is represented as a series of "If/Then" rules that may involve time relationships as well as uncertainty data The expert system offers advanced advisory, analytical, and control functions such as adaptive control, alarm interpretation and management, and cause-and-effect advi- 3:482 Index terms Sensory evaluation Links 3:9 Sensory evaluation development 1:168 Sensory evaluation technique 1:166 Serratia 2:324 Serratia marcescens 2:311 Serum point method of mix standardization Shattered curd, cottage cheese 2:94 1:197 Shelf life 3:18 3:62 3:66 test 2:378 Sherbert 3:3 Sherbets and ices formulation 2:117 Shigella 2:334 Shigella dysenteriae 2:334 3:22 3:63 3:29 3:65 3:32 Short butter 1:212 cheddar cheese 1:241 Shrunken, yogurt 1:267 Sight 1:163 color vision 1:164 Simulation 3:125 3:145 modeling 3:143 optimization 3:131 Skim milk, in acid casein production 2:292 Smell 1:162 “sniffing” 1:163 olfactory 1:162 olfactory epithelium 1:162 trigeminal 1:162 3:127 3:146 This page has been reformatted by Knovel to provide easier navigation 3:143 3:483 Index terms Links Social concern and plant construction 3:300 Sodium caseinate 2:294 in imitation milk 2:286 industrial use of 2:295 Sodium chloride butter 1:142 cheese 1:143 cryoscope 1:106 mastitis 1:116 Sodium hydroxide, in casein solubilization 2:295 Soggy, ice cream 1:226 Soil, wind, and seismic conditions and processing plants 3:298 Solubility of protein 1:289 Somatic cells 1:8 freezing point 1:106 measurement 1:115 Sorbic acid, determination, cheese 1:144 Sour, milk 1:179 Specialty equipment 3:241 butter manufacture 3:254 continuous churning 3:255 cream preparation 3:254 packaging 3:256 traditional churning 3:254 cheese 3:256 accessory equipment/mechanical innovations 3:258 cheese vats 3:257 cheesemaking systems 3:256 general processes 3:256 processed cheese 3:261 This page has been reformatted by Knovel to provide easier navigation 3:484 Index terms Links Specialty equipment (Continued) concentration and drying 3:261 cottage cheese and other cultured products 3:277 cottage cheese 3:277 fermented milk products 3:281 green cheese products 3:281 yogurt 3:279 high-temperature processing 3:281 ice cream and frozen desert equipment 3:241 batch freezers 3:247 bulky flavor addition 3:250 continuous freezers 3:249 mix freezing 3:246 mix preparation 3:242 novelty equipment 3:250 membrane separation 3:288 Specks (white), cheddar cheese 1:242 Sporobolomyces 2:382 Sporolactobacillus 2:314 Spray drying 2:275 advantages of 2:278 atomization of milk 2:276 cyclone separators 2:277 flow of air 2:276 fluid bed 2:279 industrial applications of 2:278 nitrosamines in 2:276 of infant formulas 2:282 of sodium casemate 2:295 scrubbers 2:276 temperature regimen 2:276 three-stage procedure 2:279 This page has been reformatted by Knovel to provide easier navigation 3:485 Index terms Stability of milk, thermal Links 2:260 Stabilizer action 2:87 Stabilizers 2:29 2:31 2:82 Stale, dry milk 1:269 Standardization of milk 2:265 2:271 2:267 2:273 2:270 3:14 3:54 3:33 3:58 3:37 Standards of identity Staphylococcus aureus 2:312 Starter bacteria in milk, growth of, inhibitors and 2:182 agglutination 2:185 antibiotic 2:186 bacteriocins 2:182 heat treatment 2:185 hydrogen peroxide 2:183 lactoperoxidase/thiocyanate/H/2O/2 system 2:183 lipolysis 2:182 pH 2:186 Starter culture production 2:191 bulk starter propagation 2:192 aseptic techniques 2:192 phage inhibitory media 2:193 specifically designed starter tanks 2:192 concentrated cultures 2:191 external pH control 2:195 general comments 2:196 helpful points to phage-free starters 2:196 history 2:191 internal pH control 2:195 temperature effect pH-controlled propagation of cultures Starter culture systems 2:195 2:194 2:187 This page has been reformatted by Knovel to provide easier navigation 3:486 Index terms Links Starter cultures, microbiology 2:359 function of starter cultures 2:362 flavor, aroma, and alcohol production growth and propagation 2:362 2:363 inhibition of undesirable organisms 2:363 pH control systems 2:364 phage inhibitory and phage-resistant medium (PIM/PRM) 2:365 production of lactic acid 2:362 proteolytic and lipolytic activities 2:362 genetic engineering 2:366 inhibition of starter cultures 2:365 terminology 2:359 Starter cultures and cheese making, type 2:174 Starter propagation, bulk 2:192 aseptic techniques 2:192 phage inhibitory media 2:193 specifically designed starter tanks 2:192 Starter tanks and bulk starter propagation Starters 2:192 2:13 production Statistics 2:20 3:64 Sterilization of cans 2:266 of concentrated milk 2:266 Sticky, butter 1:212 Stirred curd or granular cheddar cheese 2:200 Stokes Equation 1:47 Storage butter 1:209 ice cream 1:223 This page has been reformatted by Knovel to provide easier navigation 3:487 Index terms Links Storage of milk powder 2:278 of unsweetened condensed milk 2:266 Storage tanks 3:305 Streaky, butter 1:213 Streptococcus 2:312 Streptococcus lactis 2:362 Streptococcus salivarius subsp thermophilus 2:178 Streptococcus thermophilus Streptomyces natalaensis 2:7 2:14 2:324 2:11 2:16 2:12 2:20 2:334 Substitutes for dairy products 2:75 Successes in biotechnology 3:78 accelerated cheese maturation 3:84 bacteriocins as food preservatives 3:80 bacteriophage resistance 3:83 low-fat dairy products 3:79 Sucrose 2:313 2:79 Sugar addition to milk 2:267 sugar index 2:269 sugar number 2:269 Sugar 3:3 2:269 3:25 3:53 Sulfamethazine 3:7 Sulfhydryl compounds antioxidative properties of 2:266 inactivation of 2:273 Sulfide, Cheddar cheese 1:238 Surface color faded, butter 1:214 Surface excess 1:44 This page has been reformatted by Knovel to provide easier navigation 3:30 3:488 Index terms Links Surface tension 1:56 Surface tension 2:266 Swallowing 1:179 Sweet casein 2:292 Sweeteners 2:5 2:20 2:8 2:25 2:19 2:28 Sweeteners 3:23 3:32 3:25 3:60 3:26 3:69 See also Sugar Sweetening agents 2:76 Sweetness, of condensed milk 2:267 Swiss cheese 2:201 Swiss cheese, microbiological and biochemical changes 2:219 CO2 production 2:220 eye formation 2:221 fate of lactose 2:220 fate of proteins 2:222 flavor of swiss cheese 2:222 Syrup flavor, ice cream 1:223 T Tallowy, butter Tanford transition 1:210 1:14 Tanks 3:160 Taste 1:159 bitter 1:158 basic taste responses 1:161 filiform papillae 1:160 foliate papillae 1:160 fungiform papillae 1:160 papillae 1:160 1:160 This page has been reformatted by Knovel to provide easier navigation 3:489 Index terms Links Taste (Continued) qualities 1:160 receptors multiple qualities 1:162 salt 1:160 salty 1:158 sour 1:158 1:160 sweet 1:158 1:160 taste buds 1:159 tongue 1:160 vallate papillae 1:160 Taste buds 1:158 Tatumella 2:308 Taxonomy 2:15 Tempering, of casein 2:294 Terminology and starter cultures 2:359 Texture nomenclature 1:173 Thermal conductivity 1:60 Thermally processed low-acid foods 3:40 Thickening, of milk 2:269 Titratable acidity 1:58 Titratable acidity 2:8 1:160 2:270 2:10 Titratable acidity See acidity Tolerances 3:33 Too firm cultured products 1:252 yogurt 1:265 Too high color, ice cream 1:227 Too high flavor, ice cream 1:223 Too pale color, ice cream 1:228 Too sweet, ice cream 1:224 Too thin, cultured products 1:252 This page has been reformatted by Knovel to provide easier navigation 2:11 3:490 Index terms Links Tools 3:119 3:121 Torulopsis species 2:318 2:322 Total solids content of sodium casemate 2:295 drying methods 1:96 infra-red method 1:97 lactometer method, milk 1:96 measurement, butter 1:141 measurement, cheese 1:141 Touch 1:166 kinesthesis 1:166 somesthesis 1:166 Training Transformation and gene delivery systems 3:117 3:132 3:150 3:127 3:149 3:88 electroporation 3:88 gene delivery systems 3:89 Trends in consumption 2:4 Trichoderma 2:358 Trichosporon 2:322 Trigeminal nerves 1:162 Tuberculosis 3:119 3:135 3:5 3:14 1:40 2:25 U UHT processing 3:313 Ultrafiltered retentate and cheese 2:207 Ultrafiltration 2:271 3:69 and reverse osmosis 3:35 2:386 This page has been reformatted by Knovel to provide easier navigation 3:43 3:491 Index terms Links Ultrafiltration (Continued) asymmetric membranes 2:289 fouling of membranes 2:290 in condensed milk production 2:267 of protein processing 1:331 of whey protein concentrate 2:290 Ultrahigh temperature sterilization of milk and dairy products Unavoidable contaminants Uncertainty 2:389 3:33 3:109 3:120 3:116 3:127 3:117 3:144 Unclean cheddar cheese 1:238 cottage cheese 1:194 cultured products 1:250 milk 1:185 yogurt 1:263 Unclean/utensil, butter 1:210 Uneven sizes, cheddar cheese 1:243 Unit operations 3:121 clean-in-place 3:122 evaporator 3:122 3:136 thermal processing 3:122 3:144 3:21 3:35 U.S Department of Agriculture (USDA) Unnatural color butter 1:214 ice cream 1:228 Unnatural flavoring ice cream 1:224 yogurt 1:263 This page has been reformatted by Knovel to provide easier navigation 3:54 3:492 Index terms USDA grades Utilities and processing plant Links 3:14 3:35 3:299 V Vanilla flavor and ice cream 2:134 Vapor recompression 2:265 secondary 2:264 Vibrio cholerae 2:308 Viruses 2:318 Viscosity 1:50 2:273 low-viscosity casein 2:310 2:266 2:268 1:26 2:9 3:16 1:28 2:21 3:32 2:292 Vitamins 1:4 1:101 2:44 3:53 niacin 3:16 riboflavin 3:16 3:57 vitamin A 3:16 3:29 3:53 3:57 3:32 vitamin B-12 3:16 vitamin C 3:16 3:53 3:57 vitamin D 3:29 3:32 3:57 3:40 3:65 W Walls and doors of processing plant Warehousing and shipping 3:303 3:22 Water, addition to milk, measurement 1:105 Water-protein interactions of protein 1:282 Watery, ice cream 1:229 Wavy, butter 1:213 This page has been reformatted by Knovel to provide easier navigation 3:493 Index terms Links Weak butter 1:213 cheddar cheese 1:241 ice cream 1:226 yogurt 1:265 Weak/soft, cottage cheese 1:196 Whey butter 1:210 ice cream 1:224 Whey 2:8 2:286 3:23 3:34 3:43 3:37 3:40 “Centri” 2:298 in lactose production 2:298 modified powder 2:8 2:286 hygroscopicity of 2:289 particles 2:289 protein concentrate 2:30 demineralized 2:289 proteins 2:289 2:289 1:14 primary structure 1:14 secondary structure 1:14 sweet 2:290 taint, cheddar cheese 1:238 Wheyed-off cultured products 1:253 ice cream 1:229 Whipped toppings 3:58 Whipping 1:49 World Health Organization (WHO) 3:39 This page has been reformatted by Knovel to provide easier navigation 3:494 Index terms Links X Xanthine oxidase 2:331 Y Yarrowia lipolytica Yeasts 2:383 3:27 3:32 3:44 3:31 3:32 3:51 Yersinia enterocolitica 2:310 2:334 2:346 Yersinia pestis 2:310 Yield, of casein 2:293 Yogurt 1:254 2:1 3:32 3:48 and molds 2:318 Yeasty butter 1:211 cheddar cheese 1:239 cottage cheese 1:194 cultured products 1:250 yogurt 1:263 Yellow No 3:65 acetaldehyde 1:254 atypical color 1:265 bitter 1:258 cardboard 1:261 color leaching 1:265 composition 2:7 cooked 1:258 cultured milks and microbiology 2:384 definition 1:254 descnption 2:8 excess fruit 1:266 foreign 1:258 2:7 This page has been reformatted by Knovel to provide easier navigation 3:495 Index terms Links Yogurt (Continued) free whey frozen 1:266 2:7 fruit flavored 2:32 fruit-on-the-bottom 2:31 gellike 1:264 grainy 1:264 high acid 1:259 lacks fine flavor 1:259 lacks flavoring 1:259 lacks freshness 1:260 lacks fruit 1:266 lacks sweetness 1:260 light 2:29 live and active 2:10 low acid 2:11 1:261 low-fat 2:6 lumpy 1:267 making 1:254 manufacture equipment 3:279 metallic 1:261 2:9 2:13 nomenclature 2:9 2:12 2:13 nonfat 2:6 2:10 2:13 nutrient profile 2:7 old ingredient 1:261 oxidized 1:261 plain 2:32 plant design 2:24 processes 3:25 rancid 1:262 regulatory aspects 2:8 refrigerated 2:4 This page has been reformatted by Knovel to provide easier navigation 3:496 Index terms Links Yogurt (Continued) ropy 1:264 score card 1:255 scoring 1:254 scoring guide 1:256 shrunken 1:267 soft serve 2:5 standard of identity 2:8 stirred type 2:31 starters 2:13 texture and flavor 2:31 too firm 1:265 too high flavoring 1:262 too sweet 1:262 unclean 1:263 unnatural flavoring 1:263 weak 1:265 yeasty 1:263 1:257 2:12 Z Zeta potential 1:38 1:40 This page has been reformatted by Knovel to provide easier navigation [...]... thing?" R&D Magazine September, 64 -66 68 3 Parsaye, K., and M Chignell 1988 Expert Systems for Experts John Wiley & Sons, New York 4 Fox, J 19 86 Knowledge, decision making, and uncertainty In W A Gale (ed.), Artificial Intelligence and Statistics Addison-Wesley, Reading, MA 5 Bowerman, G., and D E Glover 1988 Putting Expert Systems into Practice Van Nostrand Reinhold, New York 6 Sletmo, K 1988 Something... computer X-ray tomography Fleischwirtschaft 69 :220-222 25 Balaban, M., A R Carrillo, and J L Kokini 1988 A computerized method to analyze the creep behavior of viscoelastic foods J Texture Stud 19:171-183 26 Doring, B., S Ehrhardt, F K Lucke, and U Schillinger 1988 Computer-assisted identification of lactic acid bacteria from meats Systemat Appl Microbiol 11 :67 -74 27 Aparicio, R 1988 Characterization... Appl Microbiol 11 :67 -74 27 Aparicio, R 1988 Characterization of food by inexact rules: the SEXIA expert system / Chemometr 3 (Suppl A): 175-192 28 Efstathiou, J 19 86 Expert system case study: the chocolate biscuit factory Journal A 27 :62 -68 29 Russo, C , C M Lanza, and F Tomaselli 1989 Use of expert systems in the quality control of typical Sicilian cheeses Industrie Allmentari 28:119-130 30 Malaureille,... computer-assisted identification of bacteria. 26 Robots have been used for automatic titrations, total solids sample preparation, gas chromatography, differential scanning calorimetry, thin-layer chromatography, nitrogen analysis, texture measurement, microbial plate counts, and fat analysis An expert system entitled SEXIA was developed to characterize certain foods, particularly olive oils.27 Up to 50 analytical... fuzzy sets, which will eliminate the restrictions of numeric ranges specified for the chemical parameters 3 .6. 3 Quality Defect Analysis Expert systems have found perhaps their most popular application in fault diagnosis or defect analysis Because this is often an activity of the quality systems department, such technology should be useful An example of an expert system designed to diagnose faults on the... consists of at least several hundred blocks Their complexity leads to difficulty when changes are made A particular configuration represents the final equations after the interactions between equipment and materials have been considered This makes it difficult to isolate and see the change of any one part or material The ISIM (Intelligent Simulation) system was created using an object-oriented methodology... material handling at the Kellogg Salada cereal plant in London, Ontario, Canada The equipment that this system controls includes a high-rise AS/RS, which has 60 00 storage locations in six aisles, a monorail system, three roller conveyor networks, and 61 special automatic and manual material handling operation stations serving the various plant production areas The control system is based on a hierarchical... 1990 Move the wrench over and pass me the computer Industry Week 239:52-54 16 Ellinger, R H 1990 Total Quality Systems Handbook—HACCP American Butter Institute/National Cheese Institute, Washington, D.C 17 Clancy, J A., and S E Ullrich 1988 Analysis and selection program for a malt quality in barley by microcomputer Cereal Chem 65 :428-430 18 Vasquez, H J 1987 An integration of system analysis and knowledge... J Dairy Sci 73 (Suppl 1): 162 20 Doluschitz, R 1990 Expert systems for management in dairy operations Comput Electron Agric 5:17-30 21 Spies, R D 1989 Use of a centralized computer system in a cereal laboratory Cereal Foods World 34:214 22 Pon, C R., O M Lukow, and D J Buckley 1989 A multichannel, computer-based system for analyzing dough rheology J Texture Stud 19:343- 360 23 Ratti, C , G H Crapiste,... Configurator lets the user design order entry screens and help text Valid values may be accessed from each screen for rapid order entry The Expert Configurator adapts to a particular pricing structure with procedures such as adding a fixed price when a particular option is selected, calculating a percent discount for a customer type or class, or calling external programs for more complex calculations Bills of