Lutein　is　a　natural　antioxidant　that　has　numerous　benefits　for　human　health.　Heterotrophic　Chlorella　sorokiniana　has　the　advantage　of　high　purity　and　production　of　lutein.　In　contrast,　the　production　of　lutein　in　Chlorella　sorokiniana　mainly　depends　on　two　factors:　biomass　productivity　and　lutein　content.　However,　conventional　approaches　such　as　the　optical　density　method　for　measuring　biomass　productivity　and　high-performance　liquid　chromatography　for　measuring　lutein　content　suffer　from　drawbacks,　including　complex　procedures　and　limited　timeliness.　A　visible　near-infrared　dual-mode　snapshot　multispectral　imaging　detection　system　was　constructed　to　rapidly　and　non-destructively　determine　the　variations　in　lutein　production　during　the　growth　process　of　Chlorella　sorokiniana.　Based　on　the　spectral　response　range,　the　visible　camera　was　used　to　obtain　the　spectral　information　image　of　lutein　content,　and　the　near-infrared　camera　was　used　to　obtain　the　spectral　information　image　of　biomass　productivity　to　build　a　visible　near-infrared　dual　mode　multispectral　dataset　containing　biomass　productivity　and　lutein　content　information.　To　address　the　issue　of　wide　spectral　range　and　limited　wavelengths　in　the　snapshot　multispectral　camera　used　in　the　system,　a　novel　approach　combining　sequential　floating　forward　selection　with　a　modified　successive　projections　algorithm　(mSPA)　was　proposed.　A　comparative　study　was　conducted,　evaluating　mSPA　against　successive　projections　algorithm,　genetic　algorithm,　and　random　frog　algorithm　for　wavelength　selection.　Multiple　linear　regression　and　extreme　learning　machine　models　were　constructed　based　on　the　selected　feature　wavelengths.　Finally,　the　optimal　predictive　models　for　biomass　productivity　and　lutein　content　were　used　to　generate　a　visualization　distribution　map　of　lutein　production　in　Chlorella　sorokiniana.　The　results　indicated　that　when　using　near-infrared　and　visible　cameras　for　biomass　productivity　and　lutein　detection　in　Chlorella　sorokiniana,　the　mSPA　algorithm　consistently　yielded　fewer　feature　wavelengths　for　both　biomass　productivity　and　lutein　and　achieved　the　highest　prediction　accuracy.　The　optimal　models　of　biomass　productivity　and　lutein　content　were　established　using　the　mSPA-selected　feature　wavelengths　in　combination　with　an　extreme　learning　machine.　The　corresponding　coefficients　of　determination　for　the　prediction　sets　were　0.　947　for　biomass　productivity　and　0.　907　for　lutein,　with　root　mean　square　errors　of　0.　698　g　.　L-1　and　0.　077　mg　.　g(-1)　and　residual　prediction　deviations　of　3.　535　and　3.　338,　respectively.　The　models　demonstrated　good　predictive　capabilities.　The　visualization　distribution　successfully　achieved　intuitive　monitoring　of　lutein　production　variations　in　Chlorella　sorokiniana　which　is　beneficial　for　online　detection　of　lutein　content　in　practical　production　scenarios.　The　mSPA　algorithm,　employed　in　the　snapshot　multispectral　detection　of　biomass　productivity　and　lutein　content　in　Chlorella　sorokiniana,　effectively　avoided　the　incorrect　selection　and　omission　of　feature　wavelengths　by　evaluating　each　sorted　wavelength　individually,　thereby　improving　the　prediction　accuracy　of　the　models.　This　approach　provides　a　new　wavelength　selection　strategy　for　applying　snapshot　multispectral　imaging　technology.