{"id":3404,"date":"2026-04-15T04:50:42","date_gmt":"2026-04-15T02:50:42","guid":{"rendered":"https:\/\/neuraldesigner.com\/blog\/milk-quality\/"},"modified":"2026-10-06T13:32:31","modified_gmt":"2026-10-06T11:32:31","slug":"milk-quality","status":"publish","type":"blog","link":"https:\/\/www.neuraldesigner.com\/blog\/milk-quality\/","title":{"rendered":"Milk impurity classification from spectral measurements"},"content":{"rendered":"<style>\n.ndb{--ndb-code-bg:#f3f7fa;--ndb-code-fg:#12354b;box-sizing:border-box;width:100%;margin-left:0;margin-right:0;padding:22px 0 14px;background:#fff;color:#1b2635;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif}\n.ndb *{box-sizing:border-box}.ndb-wrap{width:min(calc(100% - 48px),1160px);margin:0 auto}.ndb a{text-decoration:none}\n.ndb-executive{margin:0 0 34px;padding:32px;border-radius:20px;}\n.ndb-executive h2{margin:0 0 12px;font-size:30px}.ndb-executive 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background-size:cover!important;background-position:center!important; color:#193645; }\/* nd-shared-components:end *\/\n<\/style>\n<div class=\"ndb\">\n<div class=\"ndb-wrap\">\n<section class=\"ndb-executive\">\n<h2>Classify recorded pure and impure milk spectra<\/h2>\n<p>The updated example uses spectral measurements and a binary Pure\/Impure label. The saved model uses nine numeric inputs and is evaluated on 4,655 held-out rows from a 23,279-row adapted file.<\/p>\n<div class=\"ndb-kpis\">\n<div class=\"ndb-kpi\"><strong>23,279<\/strong><span>Source records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>9<\/strong><span>Encoded input values<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>4,655<\/strong><span>Testing-role records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>1<\/strong><span>Model outputs<\/span><\/div>\n<\/div>\n<div class=\"ndb-actions\"><a class=\"aui aui-button\" href=\"#7-model-deployment\">Review the current model<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-milkquality-licensed-20261006.zip\">Download model and data (ZIP)<\/a><\/div>\n<\/section>\n<ul class=\"ndb-toc\">\n<li><a href=\"#1-industrial-challenge\">Industrial challenge<\/a><\/li><li><a href=\"#2-data-set\">Data set<\/a><\/li><li><a href=\"#3-model\">Model<\/a><\/li><li><a href=\"#4-training\">Training<\/a><\/li><li><a href=\"#5-selection\">Model selection<\/a><\/li><li><a href=\"#6-testing\">Testing<\/a><\/li><li><a href=\"#7-model-deployment\">Deployment<\/a><\/li><li><a href=\"#8-limitations\">Limitations<\/a><\/li>\n<\/ul>\n<section id=\"1-industrial-challenge\" class=\"ndb-card\"><h2>1. Industrial challenge<\/h2><p>Food-science analysts can study how recorded spectral measurements distinguish the dataset labels and inspect misclassified observations. Laboratory confirmation and validated quality procedures remain separate from the tutorial model.<\/p><div class=\"ndb-value-grid\"><div class=\"ndb-value-card\"><h3>Spectral inputs<\/h3><p>Use eight diode measurements and the saved test field.<\/p><\/div><div class=\"ndb-value-card\"><h3>Label direction<\/h3><p>Interpret the positive output as Pure.<\/p><\/div><div class=\"ndb-value-card\"><h3>Independent validation<\/h3><p>Evaluate by specimen and acquisition batch.<\/p><\/div><\/div><div class=\"ndb-audience\"><span>Food science<\/span><span>Spectral analysis<\/span><span>Quality data review<\/span><\/div><div class=\"ndb-note\">Recorded spectral-label benchmark. It does not certify milk safety, absence of pathogens or regulatory conformity.<\/div><\/section>\n<section id=\"2-data-set\" class=\"ndb-card\"><h2>2. Data set<\/h2><p>The Mendeley version-1 dataset was collected for milk-impurity research with an eight-photodiode spectral sensor. The distributed CSV has 23,279 records; the repository description gives a rounded 20,000-sample overview. The model uses diode_1 through diode_8 plus the source field test, while integrationtime and referencecurrent are not inputs. The source description does not establish the physical meaning or availability timing of test.<\/p><p>Source: <a href=\"https:\/\/data.mendeley.com\/datasets\/3gxjgxkg76\/1\">A Spectral Dataset of both Liquid and Solid Milk with Impurities for some Milk Impurity Detection<\/a>. Dataset license: CC-BY-4.0. The downloadable ZIP includes the adapted data and attribution notices.<\/p><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Dataset measure<\/th><th scope=\"col\">Saved value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Analysis unit<\/th><td>spectral measurement record<\/td><\/tr><tr><th scope=\"row\">Records<\/th><td>23,279<\/td><\/tr><tr><th scope=\"row\">Raw variables<\/th><td>12<\/td><\/tr><tr><th scope=\"row\">Encoded model inputs<\/th><td>9<\/td><\/tr><tr><th scope=\"row\">Model outputs<\/th><td>1<\/td><\/tr><tr><th scope=\"row\">Training roles<\/th><td>13969<\/td><\/tr><tr><th scope=\"row\">Validation \/ selection roles<\/th><td>4655<\/td><\/tr><tr><th scope=\"row\">Testing roles<\/th><td>4655<\/td><\/tr><tr><th scope=\"row\">Unused roles<\/th><td>0<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Field<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Type<\/th><th scope=\"col\">Categories<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">integrationtime<\/th><td>None<\/td><td>Constant<\/td><td><\/td><\/tr><tr><th scope=\"row\">referencecurrent<\/th><td>None<\/td><td>Constant<\/td><td><\/td><\/tr><tr><th scope=\"row\">diode_1<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">diode_2<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">diode_3<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">diode_4<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">diode_5<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">diode_6<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">diode_7<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">diode_8<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">test<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><\/tr><tr><th scope=\"row\">label<\/th><td>Target<\/td><td>Binary<\/td><td>Impure, Pure<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-milkquality-t0-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-milkquality-t0-s2-20261006\" role=\"img\" aria-label=\"Target class distribution pie chart\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-milkquality-t0-s2-20261006.png\" alt=\"Target class distribution pie chart\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"direction\":\"clockwise\",\"hovertemplate\":\"%{label}\\u003cbr\\u003e%{value}\\u003cbr\\u003e%{percent}\\u003cextra\\u003e\\u003c\/extra\\u003e\",\"labels\":[\"Impure\",\"Pure\"],\"marker\":{\"colors\":[\"#209fdf\",\"#092d40\"]},\"showlegend\":true,\"sort\":false,\"textinfo\":\"label+percent\",\"type\":\"pie\",\"values\":[59.7400016784668,40.2599983215332]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{},\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Target class distribution pie chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"height\":500}}<\/script><\/div><figcaption>Target class distribution pie chart. Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><div class=\"ndb-note\">The saved project assigns rows to training, validation and testing as shown above. This is internal record-level evaluation; it does not demonstrate separation by subject, device, site or acquisition batch.<\/div><\/section>\n<section id=\"3-model\" class=\"ndb-card\"><h2>3. Model<\/h2><p>The model has 9 encoded inputs and 1 outputs. No architecture-selection experiment is recorded in this project. The diagram shows the topology used by the saved model.<\/p><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Layer<\/th><th scope=\"col\">Input shape<\/th><th scope=\"col\">Output shape<\/th><th scope=\"col\">Activation<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Scaling<\/th><td>9<\/td><td>9<\/td><td><\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>9<\/td><td>3<\/td><td>Tanh<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>3<\/td><td>1<\/td><td>Sigmoid<\/td><\/tr><\/tbody><\/table><\/div><p>Output values are uncalibrated model scores. For binary evaluation, the saved positive class is <strong>Pure<\/strong>.<\/p><figure class=\"ndb-architecture-figure\"><a class=\"ndb-architecture-link\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-milkquality-architecture-20261006.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-milkquality-architecture-20261006.png\" alt=\"Milk impurity classification from spectral measurements \u2014 initial network architecture\"><\/a><figcaption>Topology of the saved current model; no architecture selection is recorded.<\/figcaption><\/figure><\/section>\n<section id=\"4-training\" class=\"ndb-card\"><h2>4. Training strategy<\/h2><p>The saved training configuration uses QuasiNewton with WeightedSquaredError.<\/p><h3>Quasi-Newton method results<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Epochs number<\/th><td>43<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:01<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Minimum loss decrease<\/td><\/tr><tr><th scope=\"row\">Training error<\/th><td>0.005<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>0.002<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-milkquality-t3-s2-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-milkquality-t3-s2-20261006\" role=\"img\" aria-label=\"Quasi-Newton method error history\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-milkquality-t3-s2-20261006.png\" alt=\"Quasi-Newton method error history\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"line\":{\"color\":\"#529cc2\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Training error\",\"opacity\":1,\"showlegend\":true,\"type\":\"scatter\",\"x\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],\"y\":[0.9729999899864197,0.9330000281333923,0.6240000128746033,0.16300000250339508,0.05050000175833702,0.03280000016093254,0.01979999989271164,0.013899999670684338,0.010300000198185444,0.0081599997356534,0.006870000157505274,0.005909999832510948,0.004910000134259462,0.00395999988541007,0.0036299999337643385,0.004389999900013208,0.004769999999552965,0.005510000046342611,0.00546000013127923,0.005229999776929617,0.004629999864846468,0.004430000204592943,0.003969999961555004,0.0035099999513477087,0.0030700000934302807,0.002899999963119626,0.0029700000304728746,0.0030400000978261232,0.0030499999411404133,0.0030300000216811895,0.003010000102221966,0.002959999954327941,0.0029100000392645597,0.0028699999675154686,0.002859999891370535,0.0028899998869746923,0.0029299999587237835,0.002959999954327941,0.0030300000216811895,0.0030700000934302807,0.003019999945536256,0.0028299998957663774,0.002730000065639615]},{\"connectgaps\":false,\"line\":{\"color\":\"#ed982a\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Validation error\",\"opacity\":1,\"showlegend\":true,\"type\":\"scatter\",\"x\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],\"y\":[0.9300000071525574,0.621999979019165,0.16200000047683716,0.04919999837875366,0.031199999153614044,0.01759999990463257,0.011300000362098217,0.007499999832361937,0.005400000140070915,0.004189999774098396,0.0033499998971819878,0.0025599999353289604,0.0019499999471008778,0.002099999925121665,0.003060000017285347,0.003389999968931079,0.0040699997916817665,0.004029999952763319,0.00418000016361475,0.0037299999967217445,0.0036299999337643385,0.003280000062659383,0.002899999963119626,0.0024900001008063555,0.002300000051036477,0.002259999979287386,0.0022499999031424522,0.0022299999836832285,0.0021899999119341373,0.00215999991632998,0.002139999996870756,0.00215000007301569,0.0022299999836832285,0.0023300000466406345,0.0023900000378489494,0.002409999957308173,0.002420000033453107,0.002420000033453107,0.0023900000378489494,0.002300000051036477,0.00215000007301569,0.0021200000774115324,0.002099999925121665]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{\"orientation\":\"h\",\"x\":0.5,\"xanchor\":\"center\",\"y\":-0.24,\"yanchor\":\"top\"},\"margin\":{\"l\":95,\"r\":45,\"t\":65,\"b\":95},\"paper_bgcolor\":\"#fff\",\"plot_bgcolor\":\"#fff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"Training history\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\",\"font\":{\"size\":17}},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,42],\"title\":{\"text\":\"Epoch\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[0,1.0002],\"title\":{\"text\":\"Weighted squared error\"},\"zerolinecolor\":\"#aeb6c2\"},\"height\":500}}<\/script><\/div><figcaption>Quasi-Newton method error history. Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><\/section>\n<section id=\"5-selection\" class=\"ndb-card\"><h2>5. Model selection<\/h2><p>No model selection experiment is recorded for this version. The validation subset guides fitting where a training report is present; it is distinct from the held-out test rows.<\/p><p>On this test subset a majority-class baseline would correctly classify 59.29% of the records. This baseline does not detect both classes and is not an optimized model.<\/p><\/section>\n<section id=\"6-testing\" class=\"ndb-card\"><h2>6. Testing analysis<\/h2><p>The figures and tables below refer to the current project\u2019s saved testing analysis. The subset uses testing role 2; it contains 4655 source records.<\/p><p>ROC AUC describes ranking on this testing subset. Any optimal threshold shown in the saved ROC report was selected descriptively on that same subset; it is not an independently validated operating policy.<\/p><p>These operating-point metrics are calculated from the saved confusion counts at threshold <strong>0.5<\/strong>. The positive-label coding is stated in the model section.<\/p><h3>Confusion table<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Predicted positive<\/th><th scope=\"col\">Predicted negative<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Actual positive<\/th><td>1890 (40.6%)<\/td><td>5 (0.1%)<\/td><td>1895 (40.7%)<\/td><\/tr><tr><th scope=\"row\">Actual negative<\/th><td>1 (0.0%)<\/td><td>2759 (59.3%)<\/td><td>2760 (59.3%)<\/td><\/tr><tr><th scope=\"row\">Total<\/th><td>1891 (40.6%)<\/td><td>2764 (59.4%)<\/td><td>4655 (100.0%)<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Test measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Testing records<\/th><td>4655<\/td><\/tr><tr><th scope=\"row\">Positive cases<\/th><td>1895<\/td><\/tr><tr><th scope=\"row\">Positive prevalence<\/th><td>40.71%<\/td><\/tr><tr><th scope=\"row\">Accuracy<\/th><td>99.87%<\/td><\/tr><tr><th scope=\"row\">Sensitivity \/ recall<\/th><td>99.74%<\/td><\/tr><tr><th scope=\"row\">Specificity<\/th><td>99.96%<\/td><\/tr><tr><th scope=\"row\">Precision \/ PPV<\/th><td>99.95%<\/td><\/tr><tr><th scope=\"row\">F1 score<\/th><td>0.998<\/td><\/tr><\/tbody><\/table><\/div><h3>Area under curve<\/h3><div class=\"nd-table-scroll\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Area under curve<\/th><td>1<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-comparison-grid\"><div class=\"nd-comparison-item\"><figure class=\"nd-media-block\"><div class=\"nd-chart-bundle\" data-native-chart=\"nd-licensed-milkquality-t4-s1-20261006\"><div class=\"nd-chart-host\" id=\"nd-licensed-milkquality-t4-s1-20261006\" role=\"img\" aria-label=\"ROC chart\"><\/div><img decoding=\"async\" class=\"nd-chart-fallback\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-licensed-milkquality-t4-s1-20261006.png\" alt=\"ROC chart\"><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"fill\":\"tozeroy\",\"fillcolor\":\"rgba(82,156,194,0.300)\",\"line\":{\"color\":\"#ffffff\",\"dash\":\"solid\",\"width\":1},\"marker\":{\"color\":\"#529cc2\",\"size\":5,\"symbol\":\"circle\"},\"mode\":\"lines+markers\",\"name\":\"Random 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Native Neural Designer report for this project.<\/figcaption><\/figure><\/div><\/div><\/section>\n<section id=\"7-model-deployment\" class=\"ndb-card\"><h2>7. Model deployment<\/h2><p>Open the downloaded project in Neural Designer, inspect the dataset roles and preprocessing, then review the saved task report. Use the same input schema and category order when calculating outputs. The ZIP contains the exact current .nd, its source data and the applicable dataset notices.<\/p><p>Workflow: source measurements \u2192 schema and availability checks \u2192 model output \u2192 domain review. Keep model versions, validation evidence and incoming-data monitoring together.<\/p><div class=\"ndb-downloads\"><a class=\"aui aui-button\" href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-milkquality-licensed-20261006.zip\">Download model, data and licenses (ZIP)<\/a><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">Download Neural Designer<\/a><\/div><\/section>\n<section id=\"8-limitations\" class=\"ndb-card\"><h2>8. Scope and limitations<\/h2><p>The positive model class is Pure, not Impure. A high Pure score is not laboratory confirmation. Audit the undocumented test input, repeated spectra and specimen\/batch grouping before interpreting the near-perfect row-level result as generalization. Independent samples, instruments and laboratory labels are needed.<\/p><\/section>\n<section id=\"references\" class=\"ndb-card\"><h2>References<\/h2><ul><li><a href=\"https:\/\/data.mendeley.com\/datasets\/3gxjgxkg76\/1\">A Spectral Dataset of both Liquid and Solid Milk with Impurities for some Milk Impurity Detection<\/a>. Pranali Modi; Janvi Bhanushali; Maahi Shah; Dhruvi Dhulia; Madhuri Barochiya. 10.17632\/3gxjgxkg76.1<\/li><li>Dataset terms: Creative Commons Attribution 4.0 International. Full attribution and transformations are included in <code>LICENSES\/DATASET-LICENSE.txt<\/code>.<\/li><li><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/10\/nd-milkquality-licensed-20261006.zip\">Current Neural Designer project and saved task report<\/a>, snapshot 6 October 2026.<\/li><\/ul><\/section>\n<\/div>\n<\/div>\n<style>\n.nd-chart-bundle{width:100%;max-width:760px;min-width:0;margin:24px auto;box-sizing:border-box;position:relative;overflow-x:auto;text-align:center;background:#fff;color:#30343b}\n.nd-chart-bundle .nd-chart-host{width:100%;height:440px;min-width:0;box-sizing:border-box}\n.nd-chart-bundle:not(.is-ready) .nd-chart-host{position:absolute;visibility:hidden}\n.nd-chart-bundle.is-ready .nd-chart-fallback{display:none!important}\n.nd-chart-bundle .nd-chart-fallback{display:block;margin:0 auto!important;width:100%;height:auto;max-width:100%}\n.nd-chart-bundle>script,.nd-chart-bundle>br,.nd-chart-bundle>p:empty{display:none!important}\n@media(max-width:600px){.nd-chart-bundle{margin:20px auto}.nd-chart-bundle .nd-chart-host{height:440px}}\n.nd-chart-bundle .nd-chart-host{min-width:0!important}.nd-chart-bundle{max-width:100%}html body .nd-comparison-grid>.nd-comparison-item:only-child{flex-basis:min(760px,100%)!important;width:min(760px,100%)!important}.ndb-card details,.nds-card details{margin:20px 0}.ndb-card summary,.nds-card summary{cursor:pointer;font-weight:600}<\/style><script data-noptimize=\"1\" data-cfasync=\"false\" src=\"https:\/\/cdn.plot.ly\/plotly-basic-4.0.0.min.js\"><\/script><script data-noptimize=\"1\">(()=>{const start=()=>{if(!window.Plotly)return;document.querySelectorAll('.nd-chart-bundle').forEach(bundle=>{if(bundle.dataset.started)return;bundle.dataset.started='1';const host=bundle.querySelector('.nd-chart-host'),source=bundle.querySelector('script[type=\"application\/json\"]');if(!host||!source)return;const figure=JSON.parse(source.textContent);window.Plotly.newPlot(host,figure.data,figure.layout,figure.config).then(async()=>{const title=host.querySelector('.gtitle');if(title)host.style.minWidth=Math.ceil(title.getComputedTextLength()+48)+'px';await window.Plotly.Plots.resize(host);bundle.classList.add('is-ready');if(window.ResizeObserver){let timer;new ResizeObserver(()=>{clearTimeout(timer);timer=setTimeout(()=>window.Plotly.Plots.resize(host),80);}).observe(bundle);}}).catch(error=>{bundle.dataset.error=String(error);console.error(error);});});};const ready=()=>{if(window.Plotly){start();return;}let count=0;const timer=setInterval(()=>{if(window.Plotly){clearInterval(timer);start();}else if(++count>200)clearInterval(timer);},50);};if(document.readyState==='loading')document.addEventListener('DOMContentLoaded',ready,{once:true});else ready();})();<\/script>","protected":false},"author":24,"featured_media":4720,"template":"","categories":[],"tags":[49,43],"class_list":["post-3404","blog","type-blog","status-publish","has-post-thumbnail","hentry","tag-food","tag-industry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Milk impurity classification from spectral measurements<\/title>\n<meta name=\"description\" content=\"The updated example uses spectral measurements and a binary Pure\/Impure label. 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